Motion of microscopic particles has been a very interesting topic.In order to explain the matter wave diffraction problem better,this paper using the new method of quantum theory to studies the Double-slit diffraction of C₆₀ molecules using the new method of quantum theory.First,solve the Double-slit diffraction of C₆₀ molecules wave functions by Schrodinger equation.Then calculate the diffraction wave function after the slit of C₆₀ molecules with the Feynman path integral quantum theory.To obtain diffraction wave function of diffraction screen.Finally the analytic relation between the double-slit relative diffraction intensity and diffraction pattern position of C₆₀ molecules is obtained.The calculation results are in good agreement with the experimental data.This shows that the diffraction microscopic particles need to be studied with quantum theory method completely.This method has universality for the study of the motion of microscopic particles.It also can be used in the study of single slit diffraction,Multi-slit diffraction and grating diffraction for matter wave and other particles.

The intermolecular potential energy surface for He-AlH complex has been calculated by supermolecule method,calculation method of ab initio and CCSD(T) approach with a large basis set containing the correlation-consistent basis set aug-cc-pV5Z and the bond function set 3s3p2d1f1g.The potential energy surface has two potential well.The deeper potential well corresponds to the linear configuration of He-AlH,and R_min is 0.365 1 nm,the angle θ is 67.4°,V_min is -28.851 9 cm^-1.The shallower potential well corresponds to the linear configuration of He-Al-H,and R_min is 0.499 2 nm,V_min is -6.306 5 cm^-1.

With the development of electric vehicles,the supporting facilities,especially the charging stations,have caught more and more attention.How to locate the charging stations quickly and accurately is becoming one of the most important problems for the drivers.In accordance with the disadvantage of traditional navigators to upgrade the electronic map in time,a new type positioning system of electric vehicle charging station is proposed.The system is mainly composed of DSP,GPRS transmission module,GPS transmission module.The DSP is used to process the data.The GPRS module is used to receive the geographical position and statement of charging station while the GPS module is used to locate the charging stations and the vehicles.The system can embed the position information into the electronic map and realize the real-time location and path planning,which overcames the disadvantage of navigators.It provided the drivers with a low-cost and high-performance method of charging station positioning.

Two linearization methods,feed-forward and pre-distortion are combined in this paper,namely two pre-distortion units are added to the feed-forward linearization system.Based on the analysis of feed-forward and pre-distortion amplifier,the proposed amplifier requires quick balance of amplitude and phase of signals between different branches.In order to make the amplitude and phase quickly reach balance,adaptive control parameter network is introduced to regulate circuit parameter automatically.In this paper the principle diagram of adaptive feed-forward and pre-distortion linear power amplifier is presented,the solution of the circuit parameters when the linear amplifier work in optimum is derived by simplifying the model of it,and also the realization method of the parameters in the DSP is given.Finally,the system is simulated under ADS.Result shows that the inhibition of IMD3 of the system adding two pre-distortion units can be increased about 20 dBc than that with only feed-forward amplifier.

Evolution model of granular decision is an extension over time series sets based on classical rough set.Its purpose is to investigate the law of decisions on time series.Evolution model of granular changes the focus from static decision information system to dynamic time series for studying the regulations of evolution in decision information system which change with the time.It is a new research method for decision information system.Evolution model of granular decision has unique advantages in real application,but does not have clear description on the classification of attributes.This paper gives a concept for decision evolution sets based on evolution model of granular decision,and investigates its effects in application.

As for a decision system with both missing attribute values and mixed data types,the classical rough sets theory cannot directly do anything about it.Such a decision system was firstly defined as the incomplete mixed decision system (IMDS).Secondly,the limited neighborhood relation was proposed for composing the attribute reduction algorithm of a novel incomplete mixed decision system for IMDS,which employed the conditional entropy as the heuristic factor to make up for the positive region of decision deficiency.Based on the limited neighborhood relation,the nominal attribute and the numerical attribute and the missing attribute could be handled simultaneously by the proposed reduction algorithm without the discretization of numerical attributes or completing the incomplete data.Finally,the proposed reduction algorithm was tested on several UCI data sets.The experiment results show that the reduction algorithm can select the core attributes on the condition of keeping or improving classification accuracy.Also,how to impact the classification when specifying the value of the threshold used in the limited neighborhood relation is specified also discussed.

Based on the dominance relation rough sets theory,a new ranking model is proposed through improving the original ranking model,and some properties that the dominance matrix and comprehensive dominance degree satisfied are analyzed.Then,the concept of rank reduction is firstly proposed in this paper,and four kinds of order-preserving reductions in different levels are defined according to the strength of the order-preserving degree,then a basic algorithm is given for calculating ranking reduction.Finally,two examples are used to show the feasibility of the ranking model and ranking reduction theory.

The original reduction set may be invalid when new data are added to the decision system.Most of the existing incremental algorithms focus on increasing attributes or increasing samples.This paper analyzes the changing rules of the decision system after adding new attributes and samples.An improved incremental attribute reduction algorithm is presented with the framework of neighborhood rough sets,which can update the original reduction set dynamically by using the idea of relative positive region,and handle the two situations of incremental data mentioned above at the same time.The time complexity of the presented algorithm was analyzed and compared with the classical algorithm.The experiment results show that this conclusion accords with the attribute reduction obtained from traditional algorithm and the efficiency is improved.

Classical classification algorithm of SVM via p norm regularization usually takes the regularization parameter p as 0,1 or 2.However,large amount of experiments show that these parameters can not always achieve the best classification results.It means finding out the appropriate parameter according to specific dataset may help promote the predictive rate.LIU Jian-wei has already discussed this problem.However,as it is based on the idea of reweighed iteration,it only gets the approximate solution of the original problem.The original problem was solved from the point of optimization when 0＜p＜1.Three different kinds of SVM have been discussed and the classification results are shown with the experiments on three gene datasets.

In order to overcome premature convergence of genetic algorithm and derive better performance and higher accuracy on clustering problems,a new clustering method based on immune genetic algorithm is proposed in this paper.Immune principle is introduced into genetic clustering algorithm in the method,through adding density regulatory factor on selection operator performed according to the fitness in the mechanism.Selection probability is adjusted,and the diversity of individuals is maintained,and then the disadvantage of premature convergence is overcome.The algorithm represents individuals by improved grouping coding;based on it,a corresponding manner of initial population is formulated and appropriate genetic operators are designed,which enable the algorithm automatically finding the proper number of clusters and the proper partition from a given data set.The theoretical analysis and experiment result show that the algorithm derives better performance and higher accuracy on clustering problems.

Second-order cone programming is unsmooth convex programming problem,SOCP is widely applied in the engineering areas,such as electronic engineering,control theory,and combinational optimization.A Barzilai-Borwein algorithm for second-order conic programming is proposed.Firstly,based on the optimization condition of second-order cone programming,the second-order cone programming is equivalent to an equation system.Then,by the smooth function,the second-order conic is transmitted to a smooth function,and the equation system is equivalent to an unconstraint optimization problem.Finally,the Barzilai-Borwein algorithm is used to solve the constraint optimization problem.The Barzilai-Borwein gradient algorithm is an efficient gradient algorithm.The convergence of the algorithm has been improved.Four simulation numerical examples are used to test the new method,and the simulation results show the algorithm is feasible and efficient.

Artificial Bee Colony (ABC) algorithm is a new swarm intelligence technique inspired by the foraging behavior of a honeybee swarm.As the member of the artificial bee colony,scout bees carry out random search for discovering food sources.In order to investigate the effect of scout bees on the performance of ABC,the biological mechanism and main steps of ABC were analyzed,and then,different problem dimensions,population sizes,limit values and maximum cycle numbers were tested on the performance of ABC under the conditions of no scout bee,single scout bee and multi-scout bees conditions.Almost all the experimental results show that ABC with multi-scout bees outperforms ABC with single scout bee and ABC without scout bee on five well-known benchmark functions,meanwhile,ABC with single scout bee performs better than ABC without scout bee.Besides,the three algorithms have almost the same execution time under the same conditions due to the same order of their search complexity.These fully demonstrate that the random exploration process adopted by scout bees has positive effect on the performance of ABC.

With the rapid development of the World Wide Web,a large amount of trading information is available on the Internet.This abundance of information has created the need to help users find resources that match their individual goals and interests.This problem has been in the focus of recent research and an approach of recommendation system has been proposed.In the traditional system,users are provided with assistance in making selections according to the similarity of users' behavior and the products' information.Actually,users' interests vary gradually over time and their recent activities reveal their current interests.In this paper,a bipartite graph recommendation method is proposed based on time property,in which the time variation is taken into account and a new approach of initial resource adjustment is adopted to endow the recommended result with timeliness.Experiments show notable improvement on the Top-N hits metric.This method is not only of real value for improving the performance of recommendation system,but also has an active effect on the applications of recommendation system in E-Commerce.

Considering the low accuracy of the stream-data classification hasn't high accuracy and compression rate for data mining,the stream-data parallel classification compression algorithm was proposed based on granular analysis and storage of GEP in order to achieve faster parallel classification compression algorithm of streaming data.Firstly,get the least set of stream-data with the granular analysis method,and the approximate granular space according to division rules.Secondly,establish corresponding GEP classification model for different stream-data;Finally,send the data to compression model of GEP and compression data with dynamic storage record set form,extend serial algorithm to the parallel algorithm in MPI+OpenMP hybrid programming model,and verify the algorithm performance with the UCI data and communications bill.The experimental result shows that the effect of the classification compressions time-consuming and the compression ratio are satisfactory,the student's communication bill time-consuming is about 96 s,and the compression ratio can be achieved to 1/3.

According to the general binary algorithm's disadvantage of requiring more interactive times and more communication data,a new algorithm easy for hardware implementation was proposed.In the algorithm,the identification tree was constructed from leaves and the tags were identified on the lowest two levels.The algorithm is proved to be practical and effective by simulation and theoretical analysis.The according hardware implementation schema were also given.Compared with other algorithms',the schema's hardware acceleration ratio is up to 4,and the communication data is 12 MB/s.Testing results showed that the schema of hardware implementation was simpler,shorter identification period,higher communication ratio and hardware speedup ratio.Its application in practical engineering can be greatly promoted.

Object tracking is regarded as a classification between object and background in on-line boosting tracking algorithm (HBT) based on the Haar-like feature.The new position of the object is obtained by searching the maximum classification confidence in the candidate region.However,the exhaustive search procedure makes it difficult to ensure real-time property and result in tracking lost easily when the size of the object is too big or the speed of the object is too fast to get the maximum confidence in the candidate regions.In this paper,the particle filter is introduced into the HBT object tracking framework and an algorithm of on-line boosting object tracking based on particle filter (PFHBT) is proposed:the motion of the object is modeled and the object classification confidence is regarded as the observation of particle filter.Experimental results show that the algorithm not only improves the computing speed significantly,but also solves the problem of tracking lost caused by object fast moving effectively.

In Krause's multi-agent consensus model,every agent updates its opinion by averaging all agent opinions which are of its neighbors.In this study,the convergence properties of the model with initial connected topology are considered,and a new proof of convergence into clusters of agents in given.It followsthe rule that opinions initially confined to an interval of length L can converge to at most L clusters.The opinions of 300 autonomous agents were simulated in the conditions of five different initial topologies by using the distributed discrete-time algorithm.Simulation results show good agreement with the conclusion.

The on-chip network architecture design is completely rule-based or purely application-oriented.The former has unilaterally considered regular structure advantages,while the latter possesses unilaterally considered the application-oriented advantage.The both can not all be taken into account in the same time.This paper proposes a design method of a new communications structure for the Long-Range Link inserting in the appropriate rules topology and discusses its potential of improving the performance of the NoC.The proposed method enables the improvement of the iterative algorithm of intelligent insertion of long chain.Simulation result indicates that the improved topology can effectively delay the network congestion,significantly reduce the network latency,and improve the system throughput with less resource consumption.The improved intelligent insertion algorithm can effectively avoid the local optimal solution and get a better global optimal solution.

Histogram equalization (HE) makes the average of equalized image close to gray median regardless the original image.The gray levels of HE are degenerated and the detail of the image is lost,and the over enhancement and unnatural effect is occurred easily.For bi-histogram equalization,original image is decomposed into two sub-images based on an appropriate threshold and the sub-images are equalized independently.However,the threshold is the key issue.The iterative method for choosing the threshold of the bi-histogram is proposed previously,i.e.iterative threshold based brightness preserving bi-histogram equalization (IBBHE).Experimental results on dim medical image show that this method not only keeps the image brightness,but avoids the phenomenon of over enhancement.Based on partially overlapped sub-block histogram equalization algorithm (POSHE),an iterative threshold based partially overlapped sub-blocks bi-histogram equalization algorithm is proposed (IPOSBHE) and the iterative method is adopted in the process of sub-block histogram equalization.Experimental results on both dim medical image and bright scene image show that the local image details are enhanced,and the input image brightness is also preserved effectively.The blocking effect on the image is eliminated and the equalized image shows a more natural visual impression.

Each of the image points are corresponding with a point of feature space which is called feature vector,and the similarity of images can be measured by the distance between the feature vectors.Multi-level similarity of intelligent image retrieval system based on granular computing proposed in this paper is a scheme using unified formula and similarity to retrieve image.The method uses granular computing method not only in the image feature extraction and matching,but also in partition retrieval and multi-feature retrieval,which largely improves the image retrieval intelligent.

An offline text-independent writer recognition method based on modified grid feature is proposed.Firstly,every square in the fixed grid was marked with different symbols.Secondly,the positions of connected edge pixel pairs which satisfied the conditions of the modified grid feature were recorded by the grid around every edge pixel.Then,the probabilitic density distribution of different connected pixel pairs is obtained.The improved and weighted Chi-square distance was applied to measure the similarity of the two handwriting.The experiments on a database containing 300 writers of handwriting images demonstrate the validity of the proposed method.

Classification of license plates is an important step in License Plate Recognition system (LPR).To perform the classification task effectively,a KNN (K-Nearest Neighbor) approach based on V-optimal histogram is proposed.To improve the efficiency of classification algorithm,the multi-class problem was firstly converted to be two 2-class problems based on the reverse color information.The skewness of color moments were used as the color features of license plates,which were extracted in the HSV (hue-saturation-value) color space.And then,the two binary classification problems were resolved by two KNN classifiers.Furthermore,V-optimal histogram approach was adopted to reduce the computation complexity of KNN and improve the efficiency of classification algorithm.Comparative experiments were conducted on real images.The experimental results show that the proposed algorithm achieves good classification performances.

The present condition and its changing trend of wetland are very important to the making of policy upon the reclamation,exploitation,management and protection of wetland.In this paper,the coastal wetlands in the northern part of Jiangsu province were taken as study object and the technology about the extraction of wetland was explored by using multi-features and knowledge rules and multi-spectral Landsat 7 ETM+ images acquired on May 26,2002 and June 17,2007,in combination with the analysis upon the characteristics of wetlands and its presentation in remotely sensed imagery and the data of field investigation of the same period.At first,the extraction of wetland on the image acquired in 2002 was carried out based on knowledge rules,and then,the fusion of supervised and unsupervised classification was used to classify the image acquired in 2007.Both of the two methods obtained good results.At last,a change detection was performed on the two images using both comparison after classification and RB-NDVI-NDMI methods.The two results are in substantial agreement,but they have pros and cons.

For the cost of path in logistics networks based on Internet in which things are always varying with time,this paper proposes a time-dependent networks,and studies the logistics path planning problem based on it.For the accuracy of predict decreasing with time in reality,and when the predict level is low it is hard to get a good path planning result,a minimum-time path algorithm with arc cost predict level parameter(SWPL) and a solution with step by step strategy based on SWPL.In this algorithm,the accuracy of predict is taken into consideration,and an arc cost computing method based on traditional Dijkstra algorithm.The experimental result shows that a good path planning result can be obtained when the predict level is high or when the predict level is low but step by step solution is applied.

Considering the limitation of the optimal gradient routing algorithm for wireless sensor network,this paper proposes an energy-priority hased routing algorithm.The new algorithm considers the data packets' multi-path redundancy transmission and unbalanced energy consumption plenarily,which makes that the normal node send the information packets in the WSN to the sink node along the path of the optimal energy consumption.Simulation results show that,compared with the optimal gradient routing algorithm,the improved energy priority routing algorithm saves the network setup time and communication overhead,spares the network nodes' average energy,slows the nodes' failure time,and prolong the lifecycle of the whole network.

In the wireless sensor network with a mobile sink,the mobile sink and the sensors use their shared keys to communicate with each other securely.When some nodes are captured by the adversary,the key information maybe exposed.If each node stores large number of keys,it would reduce the network resilience.Otherwise,it may reduce the network connectivity.In order to solve this problem,this paper proposes a new key pre-distribution scheme,named N-PGPS,which is based on the algorithm of PGPS.This scheme adopts a symmetric polynomial φ(x',y') to produce a perturbed key,which enhances the network resilience effectively.In addition,changing the value of t and s in N-PGPS can also increases the network connectivity observably.The analytical results clearly show that the new schemes perform better in the aspect of network resilience and network connectivity than the existing key pre-distribution schemes in a wireless sensor network with a mobile sink.

Clustering is an important technique in cognitive radio networks.There are two reporting channels in the cluster:the channel between the secondary users and the cluster head,and the channel between the cluster head and the fusion center.This research is about the performance limitation when these two reporting channels are not ideal.Under the phenomenon of “BEP Wall”,this paper puts forward the index of “SNR Wall”.First of all,closed-form expressions for the global probabilities of detection and false alarm are derived for non identical SNR on the listening channels and non ideal reporting channels.Then the existence of SNR wall is shown.Moreover,the effect of SNR wall of the system is analyzed under different channel distributions and different modulations.It is shown that the simulated SNR wall values are very close to that of the theoretical one.The conclusions is of great value for the design of clustering algorithm.

In machine translation,the translation of polysemy is one of the most difficult problems.There are many translation errors in current online translation software.For the sake of the improvement of machine translation quality,a translation method based on “semantic combination” is used.This paper analyzes the semantic feature and the translation method of two Japanese verbs,“KIRERU” and “TAOSU”,and then,formulates translation rules for Japanese-Chinese machine translation.A small scale translation test was carried out for the translation rules in a rule-based transfer experimental machine translation system.The result indicates that our method and translation rules have respectively the accuracy rate of 85% and 83%,which is more effective than that of 21% and 61% obtained with the current online translation software.

An integrating platform was designed DICOM Standard with MEDIWSGrid project under Open Grid Services Architecture(OGSA).Through the MEDIWSGrid gateways more different information systems of the different hospitals have been integrated into a broader and virtual hospital system under the grid platform.The grid system has also realized federal authentication and authorization with distributed services,to achieve the healthcare information sharing with the platform.

Hemin aptamer was used to modify gold nanoparticles (AuNPs) to obtain a stable aptamer-nanogold probe (AussDNA).In the condition of pH8.0 Tris-HCl buffer solution containing 50 mmol/L NaCl,the substrate chain of AussDNA was cracked by hemin to produce a short single-stranded DNA (ssDNA) and then further combine with hemin to form a stable hemin-ssDNA conjugate.The AuNPs released from AussDNA would be aggregated in the condition of 50 mmol/L NaCl and exhibited a strong resonance Rayleigh scattering (RRS) peak at 367.6 nm.Under the selected conditions,the increased RRS intensity (ΔI_{367.6 nm}) was linear to hemin concentration in the range of 5～750 nmol/L,with a regression equation of ΔI_{367.6 nm}=2.4C-144.4,coefficient of 0.982 9,and a detection limit of 66 pmol/L.This RRS method was applied to determination of residual hemin in serum samples,with satisfactory results.The remnant AussDNA in the solution exhibited a strong catalytic activity on the Cu₂O particle reaction of Fehling reagent-glucose that can be monitored by RRS technique at 370 nm.When the hemin concentration increased,the AussDNA decreased,the catalysis decreased,and the RRS intensity at 370 nm decreased.The decreased RRS intensity ΔI_{370 nm} was linear to the hemin concentration in the range of 0.25～200 nmol/L,with a regression equation of ΔI_{370 nm}=5.5C+78,coefficient of 0.971 9,and a detection limit of 17 pmol/L.Accordingly,a sensitivity,selectivity,and simplicity new method of resonance Rayleigh scattering spectra to detect hemin using aptamer-modified nanogold as catalyst was established.

Wnt signaling is composed by a huge network system in which many transcription factors and regulatory proteins are involved in the pathway.The role of the DDX17 RNA helicase in the SET8 response to Wnt signaling by RT-PCR and Co-IP methods is studied.The result preliminary finds that DDX17 is crucial for activation of the Wnt target genes in mammalian cells.Additionally,there is interaction between DDX17s and SET8/TCF4/LEF1/HDAC1.

The vascular plants of Laohutiao Nature Reserve in Guangxi were comprehensively studied.The results show that 4 vascular families belong to East Asia distribution and its variants.There is no Chinese endemic family.There are 5 Chinese endemic genera and 47 Guangxi endemic species.193 rare and endangered species are found in the Reserve.14 species belong to the key protected species of Guangxi.Reevesia multinervia Y.F.Huang,sp.nov.ined.and Odontochilus napoensis H.Tang ＆ Y.F.Huang,sp.nov.ined.are two new species to be published.

In order to treat swine enzootic pneumonia and reduce the cost of pig breeding,and to promote the sustainable development of pig breeding,this study uses traditional Chinese medicine to do the sensitive drug trials to find out the corresponding sensitive traditional Chinese medicine,and establish the prevention and control of porcine respiratory pattern.The suspected swine which have asthma were divided into three groups,antibiotic and traditional Chinese medicine were used to do the relevant treatment respectively,and the effect of antibiotic and traditional Chinese medicine were compared.The experimental results show that,Chinese medicine treatment is better than that of antibiotics.It's obvious that using antibiotic for long time will create resistance for drugs,which will lead bad therapeutic effect,but traditional Chinese medicine is mild and had little after-affect,it will have obvious therapeutic effect and no resistance to drugs.

In order to know the soil aggregate characteristic on typical Karst soils,two Karst soils (terra fusca and rendzina) and a zonality soil (red soil) were selected with different soil organic carbon (SOC) content for study.Soil aggregates were obtained with dry sieving method and wet sieving method for soil stability (PAD,MWD,WSA) analyses.The results showed that,MWD and WSA were in order of rendzina＞terra fusca＞red soil(p＜0.05).On the condition of the same content of SOC,red soil was the most easily to be destroyed than terra fusca and rendzina according to the result of PAD.This implied that Karst soil aggregate mechanical stability was better than that in red soil,and the resistance of soil erosion caused by rainfall resistance in Karst soils was better than that in red soil.There are significant differences between Karst soils and red soil,and it is closely related to SOC.From the point of view of soil aggregate,it benefits the stability of Karst soil SOC.