基于Agent的网络信息收集中的“信息度”

文章首先简单描述了一个基于Agent的信息收集系统的模型。然后针对此类基于Agent的网络信息收集系统中存在的效率问题,提出了信息度的概念,用信息度反映历史查询的状态,并作为将来查询的依据,来改善在信息收集过程中的效率问题,减少将来查询的盲目性。

Impact of correlated private signals on continuous-time insider trading

A model of continuous-time insider trading in which a risk-neutral in-sider possesses two imperfect correlated signals of a risky asset is studied.By conditional expectation theory and filtering theory,we first establish three lemmas:normal corre-lation,equivalent pricing and equivalent profit,which can guarantee to turn our model into a model with insider knowing full information.Then we investigate the impact of the two correlated signals on the market equilibrium consisting of optimal insider trading strategy and semi-strong pricing rule.It shows that in the equilibrium,(1)the market depth is constant over time;(2)if the two noisy signals are not linerly correlated,then all private information of the insider is incorporated into prices in the end while the whole information on the asset value can not incorporated into prices in the end;(3)if the two noisy signals are linear correlated such that the insider can infer the whole information of the asset value,then our model turns into a model with insider knowing full information;(4)if the two noisy signals are the same then the total ex ant profit of the insider is increasing with the noise decreasing,while down to O as the noise going up to infinity;(5)if the two noisy signals are not linear correlated then with one noisy signal fixed,the total ex ante profit of the insider is single-peaked with a unique minimum with respect to the other noisy signal value,and furthermore as the noisy value going to O it gets its maximum,the profit in the case that the real value is observed.

Intra-annual Vegetation Change Characteristics in the NDVI-Ts Space:Application to Farming-Pastoral Zone in North China

Land use/land cover change (LUCC) mapping and analysis using multi-temporal normalize difference vegetation index (NDVI) data have been well documented. Recent empirical studies have documented that many new methods with high accuracy of retrieved land surface temperature ( Ts) have been developed. Thus, the combination of land surface temperature and NDVI has the greatest potential to improve the surface vegetation dynamic monitoring. In this study, the following objectives are pursued to: (1) introduce the practical method to produce the Ts, NDVI and Ts/NDVI based on remotely sensed data; (2) investigate the different retrieved result of vegetation cover information from NDVI, Ts and Ts/NDVI data sets, and analyze the intra-annual time trajectories of different vegetation cover categories in the NDVI- Ts space for farming-pastoral zone in North China, and (3) quantitative analysis the difference in using NDVI, Ts and Ts/NDVI data sets to express information based on the indices (information entropy and averaged information grads), and evaluate the relative role of Ts/NDVI data set in the discrimination of different vegetation cover categories through comparison to traditional NDVI data set.

Variable Precision Rough Set and a Fuzzy Measure of Knowledge Based on Variable Precision Rough Set

Variable precision rough set (VPRS) is an extension of rough set theory (RST). By setting threshold value β , VPRS looses the strict definition of approximate boundary in RST. Confident threshold value for β is discussed and the method for deriving decision making rules from an information system is given by an example. An approach to fuzzy measures of knowledge is proposed by applying VPRS to fuzzy sets. Some properties of this measure are studied and a pair of lower and upper approximation operato...

Gaussian mixture model clustering with completed likelihood minimum message length criterion

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

Near-surface velocity model construction based on a Monte-Carlo scheme

In tomographic statics seismic data processing, it 1s crucial to cletermme an optimum base for a near-surface model. In this paper, we consider near-surface model base determination as a global optimum problem. Given information from uphole shooting and the first-arrival times from a surface seismic survey, we present a near-surface velocity model construction method based on a Monte-Carlo sampling scheme using a layered equivalent medium assumption. Compared with traditional least-squares first-arrival tomography, this scheme can delineate a clearer, weathering-layer base, resulting in a better implementation of damming correction. Examples using synthetic and field data are used to demonstrate the effectiveness of the proposed scheme.

IMPROVED ACO SCHEDULING ALGORITHM BASED ON FLEXIBLE PROCESS

An improved ant colony optimization （ACO） algorithm is utilized in cell scheduling of the flexible manufaturing process for considering the instrument constraint, manufacturing cost and time. Firstly, the initial weighted directional diagram is set up. Secondly, the algorithm based on the dynamic pheromone updating ensures the quick convergence and the optimal solution, thus improving the feasibility and the stability of the schedule system. Aiming at reducing collaboration with external partners, decreasing the total cost and balancing the production process, the algorithm is efficient in supporting the management process of the manufacturing cell and in strengthening the information arrangement capabitity of the scheduling system. Finally, experimental results of the improved algorithm are compared with those of other algorithms.

Ontology-based similarity measure for text clustering

A method that combines category-based and keyword-based concepts for a better information retrieval system is introduced. To improve document clustering, a document similarity measure based on cosine vector and keywords frequency in documents is proposed, but also with an input ontology. The ontology is domain specific and includes a list of keywords organized by degree of importance to the categories of the ontology, and by means of semantic knowledge, the ontology can improve the effects of document similarity measure and feedback of information retrieval systems. Two approaches to evaluating the performance of this similarity measure and the comparison with standard cosine vector similarity measure are also described.

TARGET INFORMATION DIFFERENTIATING TECHNOLOGY IN INFRARED-RADAR DUAL-MODE GUIDANCE SYSTEM

An improved target tracking information differentiating system using the neural network to substitute for fuzzy rules is presented for the infrared-radar dual-mode guidance system. Since the neural network training based on the expert knowledge database is conducted off-line, the benefits for developing real-time tracking capabilities can be obtained. The network outputs the confidence degree denoted by the weight value of target information in the data fusion center according to two input variables of the measurement noise covariance and the tracking filter covariance. Simulation results show that the improved system can differentiate the target tracking information from the seeker fast and accurately.

A method based on mutual information and gradient information for medical image registration

Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.

Research on velocity and acceleration geophones and their acquired information

Based on the research of two geophone types （10 Hz moving-coil velocity and piezoelectric acceleration） and their velocity and acceleration parameters, frequency response functions have been determined, as well as the differences between them. Also, shock- vibration tests have been accomplished, not only to explain the two shock response signal differences, but also to analyze the response signal characteristics and its ability to carry information. In addition, seismic data acquisition experiments have been carried out under comparable conditions in the field. A contrast analysis of shot gathers and stack profiles acquired with the two geophone types is given in this paper. The results show that the acceleration signal from the acceleration geophone has a better advantage in terms of high signal-to-noise ratio, high accuracy, high resolution, and quantity of information to better meet current and future requirements for seismic exploration.

Two-Stage Interactive Multi-Objective Decision-Making Method Based on the Satisfactoriness Criterion

In this paper, for multi objective decision making, the defects on the commonly used interactive methods based on the satisfactoriness criterion is studied. Then a class of two stage interactive method based on the satisfactoriness criterion is proposed for improvement with the satisfactoriness criterion being determined through the collection of the decision makers preference information. An application example is presented for illustration of applicability of the method.

Study on Chironomid Larvae Recognition Based on DWT and Improved KNN

A chironomid larvae images recognition method based on wavelet energy feature and improved KNN is developed. Wavelet decomposition and color information entropy are selected to construct vectors for KNN that is used to classify of the images. The distance function is modified according to the weight determined by the correlation degree between feature and class, which effectively improves classification accuracy. The result shows the mean accuracy of classification rate is up to 95.41% for freshwater plankton images, such as chironomid larvae, cyclops and harpacticoida.

Coherence estimation algorithm using Kendall's concordance measurement on seismic data

The coherence method is always used to describe the discontinuity and heterogeneity of seismic data. In traditional coherence methods, a linear correlation coefficient is always used to measure the relationship between two random variables （i.e., between two seismic traces）. However, mathematically speaking, a linear correlation coefficient cannot be applied to describe nonlinear relationships between variables. In order to overcome this limitation of liner correlation coefficient. We proposed an improved concordance measurement algorithm based on Kendall＇s tau. That mainly concern the sensitivity of the liner correlation coefficient and concordance measurements on the waveform. Using two designed numerical models tests sensitivity of waveform similarity affected by these two factors. The analysis of both the numerical model results and real seismic data processing suggest that the proposed method, combining information divergence measurement, can not only precisely characterize the variations of waveform and the heterogeneity of an underground geological body, but also does so with high resolution. In addition, we verified its effectiveness by the actual application of real seismic data from the north of China.

Investigation on Physiological Status of Regional Vegetation Using Pushbroom Hyperspectral Imager Data

To extract vegetation pigment concentration and physiological status has been studied in two test areas covered with swamp and flourish vegetation using pushbroom hyperspectral imager (PHI) data which flied in September of 2000 at Daxing'anling district of Heilongjiang Province, China. The ratio analysis of reflectance spectra (RARS) indices, which were put forward by Chappelle et al (1992), are chosen in this paper owing to their effect and simpleness against both comparison with various methods and techniques for exploration of pigment concentration and characteristics of PHI data. The correlation coefficients between RARS indices and pigment concentration of vegetation were up to 0.8. The new RARS indices modes are established in the two test areas using both PHI data and spectra of different vegetations measured in the field. The indices' parameter images of chlorophyll a (Chl a), chlorophyll b (Chl b) and carotenoids (Cars) of the test areas covered with swamp and flourish vegetation are acquired by the new RARS indices modes. Furthermore, the regional concentration of Chl a and Chl b are extracted and quantified using regression equations between RARS indices and pigment concentrations, which were built by Blackburn (1998). The results showed the physiological status and variety clearly, and are in good agreement with the distribution of vegetation in the field.

A Heterogeneous Information Fusion Deep Reinforcement Learning for Intelligent Frequency Selection of HF Communication

The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network(DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning(HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.

A strip thickness prediction method of hot rolling based on D_S information reconstruction

To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method(DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, iba Analyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment(BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

Mining production information management system in an open pit based on GIS/GPS/GPRS/RFID

Using GIS, GPS, GPRS and RFID, a dynamic information management system of digital mining in an open pit was designed and developed.A linear programming model was set up in a practical application.By the model, the system can automatically draw up production plan of ore blending well every day.The system can monitor and dispatch open-pit trucks and shovels well, and can play back their historical paths.It can monitor and control the process of mining production in real time.By RFID, the system can also count the number of trucks'delivery and shovels'loading automatically.Experiments on real scenes show that the performance of this system is stable and can satisfy production standards of mining in open pits.

Mutual Information for Image Registration

Image registration is the overlaying of two images of the same scene taken at different times or by different sensors. It is one of the essential steps in information processing in remote sensing. To attain a highly accurate, reliable and low computation cost in image registration a suitable and similarity metric and reduction in search data and search space is required. In this paper, the author shows that if the right bin size is chosen, mutual information can be more robust than correlation in the registration of multi-temporal images. The author also compares the sensitivity of mutual information and correlation to Gaussian and multiplicative speckle noise. The author investigates automatic subimage selection as a reduction in search data strategy. The author proposes a measure, called alienability, which shows the ability ofa subimage to provide reliable registration. Alternate subimage selection methods such as using gradient, entropy and variance are also investigated. The author furthermore looks into a search space strategy using a gradient approach to maximize mutual information and show our first results.

Test Stability of Uster Advanced Fiber Information System(AFIS)

To study test stability of Advanced Fiber Information System(AFIS),card sliver produced in two experiments(12 plans in each experiment)were tested by AFIS.By a statistic analysis of the test results,the number of test times that can get a reliable test reliability(hereinafter this number of test times is referred to as Reliable Test Times,RTT)of test parameters and the coefficient of variation(CV%)values of 30 test results of each experiment plan were obtained.It's concluded that some parameters,such as length,seed coat nep(SCN)size,nep size and immature fiber content(IFC),etc.are very reliable by a test of ten or more times,but other parameters,such as SCN content,trash content,and visible foreign matter(VFM)content,etc.are not reliable until they are tested over 100 times.