决策树法在现代企业管理中的应用

现代企业的经营方向面临着许多可供选择的方案,如何用最少的资源,赢得最大的利润以及最大限度的降低企业的经营风险,是企业决策者经常面对的决策问题,决策树法能简单明了的帮助企业决策层分析企业的经营风险和经营方向。

Quantum Teleportation Schemes of an N-Particle State via Three-Particle General W States

Two schemes of teleporting an N-particle arbitrary and unknown state are proposed when N groups of three- particle general W states are utilized as quantum channels. In the first scheme, the quantum channels are shared by the sender and the recipient. After the sender＇s Bell-state measurements on his （her） particles, the recipient carries out unitary transformations on his （her） particles. And then, the recipient performs computational basis measurements to realize the teleportation. The recipient can recover the state on either of particle sequences with the equal maximal probability of successful teleportation if he （she） performs appropriate unitary transformations. In the second scheme, the quantum channels are shared by the sender, the recipient and the third ones. After the sender＇s Be11-state measurements and the third ones＇ computational basis measurements if they agree to cooperate, the recipient will introduce auxiliary particles and carry out appropriate unitary transformations. Finally, the recipient performs computational basis measurements to fulfill the teleportation. The second scheme can be realized if and only if the third ones agree to cooperate with the recipient.

Studying the Mechanical Properties of Jute/Cotton Blended Yarns Using the Weibull Model

The phenomenon of a material failure varies with its gauge length in use. In the present study, the breaking strengths of J30/C70 and J55/C45 blended yarns spun by the ring spinning technology were investigated with the employment of the equations derived from the Weibull theory. The four probability estimators of the Weibull distribution were also evaluated. The results showed that the estimator （2a） was acceptable for calculating the Weibull modulus of the jute/cotton blended yarns. With the exception of breaking strength of the J30/C70 at the gauge length of 200 ram, the breaking strengths of both blended yarn could be predicted accurately by the Eqs. （9） or （10） in this paper.

RESEARCH PROGRESS ON THE STRUCTURE AND INTENSITY CHANGE FOR THE LANDFALLING TROPICAL CYCLONES

Landfalling tropical cyclones(LTCs)include those TCs approaching the land and moving across the coast.Structure and intensity change for LTCs include change of the eye wall,spiral rain band,mesoscale vortices,low-layer shear lines and tornadoes in the envelope region of TC,pre-TC squall lines,remote rain bands,core region intensity and extratropical transition(ET)processes,etc.Structure and intensity change of TC are mainly affected by three aspects,namely,environmental effects,inner core dynamics and underlying surface forcing.Structure and intensity change of coastal TCs will be especially affected by seaboard topography,oceanic stratification above the continental shelf and cold dry continental airflow,etc.Rapid changes of TC intensity,including rapid intensification and sudden weakening and dissipation,are the small probability events which are in lack of effective forecasting techniques up to now.Diagnostic analysis and mechanism study will help improve the understanding and prediction of the rapid change phenomena in TCs.

Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry

To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.

Trajectory planning of mobile manipulators in constrained workspace

The cooperative motion planning of nonholonomic wheeled mobile manipulators （WMM） with kinematic redundaney in the constrained workspace was studied. A trajectory planning method combining the probabilistic planning and the solving of inverse kinematic equations was presented. First, the probabilistie planning method was used to determine the guide configurations; then the switching objective function was defined and the path of the end-effector was constructed based on these guide configurations ; finally, the inverse kinematic equations of the WMM were solved using the gradient projection method, and the obstacle avoidance trajectory for joints of the WMM was obtained. Simulation results were given to demonstrate the effectiveness of the proposed method.

Probability Density and Statistical Properties for a Three-State Markovian Noise and Escape of Particles for a System Driven by This Noise

A three-state Markovian noise is investigated.Its probability density and statistical properties are obtained. Escape of particles for a system with potential barrier only driven by this noise is investigated.It is shown that,in some circumstances,this noise can make the particles escape over the potential barrier;but in other circumstances,it cannot. Resonant activation phenomenon appears for the system considered by us.

A NOVEL INTEGER FREQUENCY OFFSET ESTIMATOR FOR OFDM

One of the principal disadvantages of Orthogonal Frequency Division Multiplexing (OFDM) is very sensitive to carrier frequency offset. The integer frequency offset has no effect on the orthogonality among the subcarriers, but it causes a circular shift and phase rotation of the received data symbols sequence, resulting in a Bit Error Rate(BER) of 0.5. In this paper,a novel integer frequency offset estimator for OFDM is derived based on maximum likelihood estimation technique and exploration of the differential relation between two consecutive OFDM data symbol sequences in frequency domain. Its performance is compared with the conventional method by computer simulations for the additive white Gaussian noise channel and a multipath fading channel. Simulation results show that the performance of the proposed estimator is better than the conventional estimator.

Memory Safety Based on Probabilistic Memory Allocation

Some unsafe languages,like C and C++,let programmers maximize performance but are vulnerable to memory errors which can lead to program crashes and unpredictable behavior.Aiming to solve the problem,traditional memory allocating strategy is improved and a new probabilistic memory allocation technology is presented.By combining random memory allocating algorithm and virtual memory,memory errors are avoided in all probability during software executing.By replacing default memory allocator to manage allocation of heap memory,buffer overflows and dangling pointers are prevented.Experiments show it is better than Diehard of the following aspects:memory errors prevention,performance in memory allocation set and ability of controlling working set.So probabilistic memory allocation is a valid memory errors prevention technology and it can tolerate memory errors and provide probabilistic memory safety effectively.

AGGREGATE IMAGE BASED TEXTURE IDENTIFICATION USING GRAY LEVEL CO-OCCURRENCE PROBABILITY AND BP NEURAL NETWORK

Classifying the texture of granules in 2D images has aroused manifold research atten-tion for its technical challenges in image processing areas.This letter presents an aggregate texture identification approach by jointly using Gray Level Co-occurrence Probability(GLCP) and BP neural network techniques.First, up to 8 GLCP-associated texture feature parameters are defined and computed, and these consequent parameters next serve as the inputs feeding to the BP neural network to calculate the similarity to any of given aggregate texture type.A finite number of aggregate images of 3 kinds, with each containing specific type of mineral particles, are put to the identification test, experimentally proving the feasibility and robustness of the proposed method.

Geom/G_1,G_2/1/1 REPAIRABLE ERLANG LOSS SYSTEM WITH CATASTROPHE AND SECOND OPTIONAL SERVICE

This paper studies a single server discrete-time Erlang loss system with Bernoulli arrival process and no waiting space. The server in the system is assumed to provide two different types of services, namely essential and optional services, to the customer. During the operation of the system, the arrival of the catastrophe will break the system down and simultaneously induce customer to leave the system immediately. Using a new type discrete supplementary variable technique, the authors obtain some performance characteristics of the queueing system, including the steady-state availability and failure frequency of the system, the steady-state probabilities for the server being idle, busy, breakdown and the loss probability of the system etc. Finally, by the numerical examples, the authors study the influence of the system parameters on several performance measures.

A knowledge push technology based on applicable probability matching and multidimensional context driving

Actively pushing design knowledge to designers in the design process, what we call ‘knowledge push＇, can help improve the efficiency and quality of intelligent product design. A knowledge push technology usually includes matching of related knowledge and proper pushing of matching results. Existing approaches on knowledge matching commonly have a lack of intelligence. Also, the pushing of matching results is less personalized. In this paper, we propose a knowledge push technology based on applicable probability matching and multidimensional context driving. By building a training sample set, including knowledge description vectors, case feature vectors, and the mapping Boolean matrix, two probability values, application and non-application, were calculated via a Bayesian theorem to describe the matching degree between knowledge and content. The push results were defined by the comparison between two probability values. The hierarchical design content models were built to filter the knowledge in push results. The rules of personalized knowledge push were sorted by multidimensional contexts, which include design knowledge, design context, design content, and the designer. A knowledge push system based on intellectualized design of CNC machine tools was used to confirm the feasibility of the proposed technology in engineering applications.

Image retrieval based on multi-concept detector and semantic correlation

With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.

Why Matter Occupies so Large a Volume?

The paper represents a rigorous treatment of the underlying quantum theory, not just in words but providing the underlying technical details, as to why matter occupies so large a volume and its intimate connection with the Pauli exclusion principle, as more and more matter is put together, as well as of the contraction or shrinkage of ＂bosonic matter＂, upon collapse, for which the Panli exclusion is abolished. From the derived explicit bounds of integrals of powers of the particle number densities, explicit bounds on probabilities of the occurrences of the events just described are extracted. These probabilities lead one to infer the change of the ＂size＂ or extension of such matter, upon expansion or contraction, respectively, as their content is increased.