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In this paper, we present a novel training method based on Baum-Welch algorithm for hidden Markov models (HMM), named as Comprehensive HMM (CompHMM), which changes the traditional approach of training HMM from positive examples only to be able to utilize both positive and negative examples in training HMMs. By comparison, our method outperformed the standard Baum-Welch method and another HMM discriminative training method significantly through both synthetic and real data in membership prediction task.

HVDC near-zone assets are part of the assets in AC power grids that serve HVDC transmission projects primarily. The division of HVDC near-zone assets directly affects the approval of HVDC transmission prices and provincial grid transmission prices, which in turn affects the orderly delivery of clean energy. Aiming at the current problem of unclear division of HVDC near-zone assets in Southwest Regional Power Grid, a identification method based on power flow contribution was proposed in this paper. Firstly, a reasonable power flow contribution evaluation indicator system was established, and the G1 method and the entropy weight method was used to calculate the subjective and objective weights of the evaluation indicators respectively. At the same time, the subjective and objective weights were combined weightedly, effectively avoiding that the evaluation results are too subjective or too objective. Secondly, the TOPSIS method was used to comprehensively evaluate the power flow contribution when the equipment serves the HVDC transmission project and the AC power grid respectively. Finally, the main service object of the equipment was judged according to the power flow contribution, and the HVDC near-zone assets were identified. The calculation example verifies the feasibility of the method.

Aiming at the problem of the lack of power load data in the forecasting of the delivery capacity of the sending end area and the load level forecasting of the receiving end during the clean energy delivery in the southwestern region, this paper gives the power load data recovery from the perspective of time series data characteristic analysis and modeling and estimation method. Based on the analysis of the multi-scale time series characteristics of the annual, monthly, and daily fluctuations of the electric load, the multi-scale time series characteristic modeling of the electric load is established. Spline interpolation is introduced to solve the non-parametric and variable coefficient problems of the load model, and the estimation method of the key parameters in the load model is given. According to the obtained load recovery model, a recovery method for missing data of weekly power load is proposed, and the idea of recovering daily load data is given. The actual calculation examples prove that the method proposed in this paper is accurate and effective.

This paper proposes an improved trajectory tracking control method for the autonomous mobile robot with non-holonomic constraints and uncertain models by using the double loop control of position loop and speed loop, which can deal with bounded disturbances and unstructured problems of the dynamics. The position loop uses PID control, the speed loop uses adaptive backstepping control, and LQR approaches is used to optimize the parameters of the backstepping control. The simulation results show that the trajectory under control can track the desired trajectory asymptotically, linear velocity error and angular velocity error can converge to zero asymptotically. When the model parameters change, the system can adjust adaptively.

In order to meet the rapid growth of distributed energy resources (DER), effective monitoring and integration of information from DER is becoming essential. Therefore, an innovative architecture of Internet of things (IoT) based IEC 61850 extensible messaging and presence protocol (XMPP) cloud platform is proposed and constructed in Taiwan Power Research Institute (TPRI) of Taiwan Power Company (TPC). In this paper, we describe IEC 61850 XMPP and security enhancements. We also describe the platform and site implementation is discussed as well. Performance analysis and lessons learned are conducted in this paper.

Cyclomatic complexity (CC) is often used as a factor to evaluate the quality of source code. Many researchers have studied the relationships between CC and LOC, between CC and basic testing paths, and between CC and code maintainability etc. However, few researchers studied how software evolution affects CC. In this paper, we propose a methodology based on source code change analysis and develop a supporting tool, called CCEvaluator, to evaluate CC variation during software evolution. By empirical studies on six pieces of typical open source projects, a series of interesting findings including six commonness and five differences have been obtained. To explain why these commonness and differences are produced during software evolution, code change information among successive versions are captured and used in this paper.

Underwater trajectory estimation is a challenging problem and established technologies developed for terrestrial systems cannot be used underwater. Known trajectory estimation techniques suffer from high cost in the setting up the infrastructures. In this paper, we propose and investigate a receiver trajectory estimation technique based on grid map localization and data smoothing approach. In the localization the receiver estimates the Channel State Information and compares it with a CSI pre-computed on a grid of points covering the area of interest; the best match indicates the estimated location. For a dynamic receiver moving in the area of interest, with estimated locations of sample points along the receiver trajectory, smoothing approach based on P-splines is applied to recover the trajectory. Numerical results show that an accurate estimated trajectory can be achieved.

Urban solid waste prevention and recycling are needed at the local scale rather than heavily relying on the treatment and disposal at the regional scale. Differentiated collection is necessary but complicates the current schemes of waste collection. To bridge this gap and construct an efficient, effective, and sustainable scheme, this study developed a smart and green system (SGS) which embodies the in-depth integration of various informatics and the strategy of sustainable waste management. The proposed SGS is able to gather the massive volumes of data and information from different application scenes of waste separation and differentiated collection, with the aid of sensor network technologies to timely detect state parameters throughout a waste collection system and the rate of waste separation and recycling. In-network data processing and cloud computing contribute to managing the communications among all collection nodes, vehicles, and crews in a secure, scalable and highly-available environment. The proposed SGS can deal with well-structured, semi-structured, and even poorly structured problems in a more efficient, collaborative, and resilient way with the aid of a position-navigation-timing system. Integrated three-level SGS architecture with a scalable sensor network is proposed in this study as an instruction of developing SGS for sustainable waste management.

Renewable energy plays a critical role in Smart Grid. As the penetration of renewable energy increases, the management of operations becomes a critical issue. One of the most important task is interoperability among different models and vendors when the system is needed to be centrally controlled. Standardization is the world's trend to solve interoperability problems. This study focuses on standardizing the wind power energy communications, and the standard specifically designed for wind power is the IEC 61400-25. The aim of this study is to develop a procedure for implementing a gateway that transforms traditional communication protocol to the international standard for the transitioning stage.

Traditional spatio-temporal data model (STDM) is based on relational database, it is hard to convert problem domain model to relational model, which result in complicated query and low expansibility. In this regard, we propose the spatio-temporal data model based on graph database. The data model integrates TGIS's three key elements: space, time and attributes, and expressed spatio-temporal characteristics of TGIS explicitly. Finally, this paper gives a particular description of logistics distribution route optimization. Experimental results show that the model is proved to be appropriate for expressing the spatio-temporal process of world.

Due to the traditional wireless networks, fixed allocation of spectrum is one of the main reasons causing low utilization of spectrum. To solve the problem, a new wireless communication model has been proposed, which called Cognitive Radio Networks (CRN). CRN adopts Dynamic Spectrum Access (DSA) technology, thus it can flexibly use the spectrum which primary user temporarily unused. In cognitive radio networks, due to each secondary user (SU) has different location and surrounding spectrum environment, it may have variety of available channels. How to assign these available channels is the crucial point of system performance. However, existing methods doesn't consider the problem of multipath fading, therefore, this study proposed an improved channel allocation mechanism. We consider the received signal strength to define the channel access priority of secondary users applied by fuzzy theory. Finally, the simulation results show the superior of our approach and verify the effectiveness of the proposed mechanism.

Identifying genes that are associated with two or more diseases can shed lights on understanding the pathobiological mechanisms of these diseases. In this work we present a novel method to predict missing common genes for disease pairs. The method formulates searching for missing common genes as an optimization problem to minimize a network based module separation between two subgraphs formed by mapping the disease associated genes onto the interactome. Tested on a dataset of more than 600 disease pairs using cross-validation, it is shown that the method achieves an average ROC score of 0.95.

In cloud computing environment, because of the open environment and centralized organizational structure, it is difficult to build trust relationships between cloud services and users. In this paper, a model to evaluate the credibility of services in cloud computing environments based on multi-factor is proposed. The trust evidence, the scene and time were classified as service credibility impact factors. After a series of calculation, the final results can fully reflect the extent of trust relationships between users and services in a period of time. Simulation experiments were conducted and the simulation results proved that the proposed model can effectively reflect the affects of the user's personal preferences and control the adverse effects caused by the service updating.

Software evolution is always happening during its lifetime. In a software evolution process, the change in software structure often leads to software quality degradation, makes it difficult to maintain or transfer to other platform. In this paper, we propose a technique to evaluate software evolution based on architecture metric. We split the whole architecture evolution process into a series of atomic evolution operation steps, analyze the impact of each atomic change operation with examples, and then find out the general evolution trend. Our purpose is to analyze how architecture changes influence the relevant software quality attributes, which helps to maintain good software quality and keep software healthily evolving.

Changes to a composite service need to be well analyzed in order to ensure its trust is maintained. In this paper, we propose a trust analysis model to analyze the impact of three kinds of evolution operations performed on a composite service: business process changes, binding changes and interface changes. A trust dependency graph is introduced to analyze the impact on the trust of component services; a XBFG (eXtensible BPEL Flow Graph) is introduced to evaluate the impact on the trust of the composite service. Three metrics are introduced to evaluate the impact of these evolution operations. The case study shows how these evolution operations affect the trust of other component services and the composite service.

Underwater acoustic (UWA) channels are characterized by spreading of received signals in space (angle of arrival) and delay. The spread is often limited to a small number of clusters. For combining signals spread in delay, equalizers are used. To separate space clusters, antenna arrays are used. In this paper, a receiver is investigated that exploits space-time clustering in UWA channels. The investigated receiver is designed for a communication system using guard-free orthogonal frequency division multiplexing (OFDM) signals with superimposed pilot symbols and a vertical linear array of hydrophones. We investigate and compare performance of several space-time processing techniques and show that the space-time cluster combining demonstrates the best performance. The comparison has been done using signals recorded on a 14-element vertical antenna array in sea trials at a distance of 105 km transmitted by a fast moving transducer. In these conditions, with the receiver, exploiting the cluster combining, error free transmission is achieved at a data rate of 0.33 bits/s/Hz. We also show that the cluster combining approach allows reduction in the complexity of the receiver.

Modelling relational data is a common task for many machine learning problems. In this work, we focus on learning pairwise relations between two entities, with deep neural networks. To incorporate the structural properties in the data that represent two entities concatenated together, two separate stacked autoencoders are introduced in parallel to extract individual features, which are then fed into a deep neural network for classification. The method is applied to a specific problem: whether two input handwritten digits differ by one. We tested the performance on a dataset generated from handwritten digits in MNIST, which is a widely used dataset for testing different machine learning techniques and pattern recognition methods. We compared with several different machine learning algorithms, including logistic regression and support vector machines, on this handwritten digit subtraction (HDS) dataset. The results showed that deep neural networks outperformed other methods, and in particular, the deep neural networks fitted with two separate autoencoders in parallel increased the prediction accuracy from 85.83%, which was achieved by a standard neural network with a single stacked autoencoder, to 88.27%.

Reconstruction of PPI networks is a central task in systems biology, and inference from multiple heterogeneous data sources offers a promising computational approach to making de novo PPI prediction by leveraging complementary information and the partial network structure. However, how to quickly learn weights for heterogeneous data sources remains a challenge. In this work, we developed a method to infer de novo PPIs by combining multiple data sources represented in kernel format and obtaining optimal weights based on random walk over the existing partial network. Our proposed method utilizes Baker algorithm and the training data to construct a transition matrix which constrains how a random walk would traverse the partial network. Multiple heterogeneous features for the proteins in the network, including gene expression and Pfam domain profiles, are then combined into the form of a weighted kernel, which provides a new “adjacency matrix” for the whole network but is required to comply with the transition matrix on the part of the training subnetwork. This requirement is met by adjusting the weights to minimize the element-wise difference between the transition matrix and the weighted kernel. The minimization problem is solved by linear programming. The weighted kernel is then transformed to regularized Laplacian (RL) kernel to infer missing or new edges in the PPI network. The results on synthetic data and real data from Yeast show that the accuracy of PPI prediction measured by AUC is increased by up to 19% as compared to a control method without using optimal weights. Moreover, the weights learned by our method Weight Optimization by Linear Programming (WOLP) are very consistent with that learned by sampling, and can provide insights into the relations between PPIs and various feature kernels, thereby improving PPI prediction.

In traditional wireless networks, fixed allocation of spectrum is one of the main reason causing low utilization of spectrum. In order to solve this problem, a new wireless communication model has been proposed, which called Cognitive Radio Networks (CRN). CRN adopts Dynamic Spectrum Access (DSA) technology, thus it can flexibly use the spectrum which primary user temporarily unused. In cognitive radio networks, due to each secondary user (SU) has different location and surrounding spectrum environment, it may have variety of available channels. How to assign these available channels is the crucial point of system performance. However, existing methods doesn't consider the problem of multipath fading; therefore, this study proposed an improved channel allocation scheme. We consider the received signal strength to define the channel access priority of secondary users applied by fuzzy theory. Finally, the simulation results show the superior of our approach and verify the effectiveness of the proposed scheme.

In this work we developed lattice models with novel scoring matrices used in conjunction with genetic algorithm for protein structure prediction. Specifically, we incorporated into the propensity scoring matrices the knowledge that hydrophobic residues should be near the core of the folded protein while hydrophilic residues should be on the outside, thus scored a conformation generated by the genetic algorithm by taking into account residues' positions in relation to the center of the conformation, which we define as the centroid of all residues. Results from the new scoring matrices show noticeable improvements, many significantly, over the standard HP lattice model and the more recent HPNX and hHPNX models.

In this article, we paid more attention on researching how to monitor Web services for checking conformance, where an AOP-based run-time monitoring framework was proposed and explored. In the framework, WS-Policy was firstly used to express the monitoring requirement, then monitoring requirement was described as AOP monitoring logic, Active BPEL engine was extended to weave the monitoring logic and the service core execution logic dynamically, a checking algorithm was introduced to analyze whether the user's monitoring requirement was satisfied according to MREG (Monitoring Requirement Expression Graph) and EMSC (Extended Message Sequence Charts), and finally, some control and modification measures were adopted in order to rise the quality of the service composition.

Trust is one of the most critical quality factors for service requestors when they select the services from a large pool of Web services. However, existing web service trust models either do not focus on satisfying user's preferences for different quality of service (QoS) attributes, or do not pay enough attention to the impact of malicious ratings on trust evaluation. To address these gaps, a user-oriented trust model considering user's preferences and false ratings is proposed in this paper. The model introduces an approach to automatically mine user's preferences from their requirements, the preferences are used to determine the weights of each QoS attribute when integrating local trust into the multi-dimensional QoS attributes. The local trust on a service for the user is derived by combining the trust on QoS attributes and the trust on user's ratings. In this model, the users are classified into different groups according to their preferences, the honesty of each group is assessed by filtering out dishonest users using a hybrid approach which combines rating consistency clustering and an average method. To calculate the global trustworthiness of a service for the users group, the weight of ratings is dynamically adjusted according to the results of honesty assessment. The simulation results indicate that the model works well on personalized evaluation of trust, and it can effectively dilute the influence of malicious ratings.

Identification of interacting residues involved in protein-protein and protein-ligand interaction is critical for the prediction and understanding of the interaction and has practical impact on mutagenesis and drug design. In this work, we introduce a new decoding algorithm, ETB-Viterbi, with early trace back mechanism built into interaction profile hidden Markov models (ipHMMs) that can incorporate the longdistance correlations between interacting residues to improve prediction accuracy. The method was applied and tested to a set of domain-domain interaction families from the 3DID database, and showed statistically significant improvement in accuracy measured by F-score. To gauge and assess the method's effectiveness in capturing the correlation signals, sets of simulated data based on the 3DID dataset with controllable correlation between interacting residues were also used, and it was demonstrated that the prediction consistently improves as the correlations increase.

In allusion to the problem of formulation of flood disaster classification standards, this paper proposes a method to deal with a fuzzy clustering iterative model and chaotic differential evolution algorithm. After analyzed a flooding example with fuzzy clustering iterative model, the classification standards was developed according to the optimal fuzzy clustering center matrix and the standard was verified by projection pursuit cluster model. Data analysis showed that the classification is reasonable and effective.

Identifying micro RNAs (miRNAs) and their target genes plays an increasingly important role in better understanding the regulatory activities in the cell. Most computational methods focus on the sequence complementarity between miRNAs and target genes without using actual expression data, which, even when used, has been primarily just for validation of the predicted relationship between specific miRNAs and genes. Recent findings have shown that many targets are missed by sequence-based approaches. In this work, we present a robust method to predict and evaluatemi RNA-gene pairs based on their positional (time-course)expression data from next-generation sequencing and DNA microarray. The method first uses K-means clustering to group miRNAs and genes respectively, and then assigns miRNA-gene pairs to a bipartite graph with statistical scoring. The method is tested by ten-fold cross validation on two datasets in Arabidopsis, achieving a performance of about 0.70 ROCscore.

In this paper, view-dependent multi-resolution data visualization techniques are discussed, including I/O optimized multi-resolution data organizing, view-dependent clipping methods with the viewing frustum, rendering with adaptive resolution and fast search methods for target datasets. A specified strategy is chosen for obtaining parameters of the viewing frustum and transferring with multiresolution operator plug-in. Bounding-box method and parent-child relationship between different resolution level are used to speed up searching target datasets and clipping viewing frustum.

In this paper, a real-time stereo visual system which is capable of recognizing user's gesture is presented. In the proposed system, the image stream of user is captured by two off-the-shelf webcams to calculate the depth information which is used to detect the user's position and head. The operating area of user's hand is dynamically determined based on the head position of user. A circular template is applied to search the center point of user's palm, which would be corrected by the directional weighting. The trajectories formed by the motion of user's hand are smoothed by averaging and B-spline curve fitting. Finally, several basic gestures are tested by 12 participants to verify the performance of the proposed system.

Reliability has become a major quality metric for Web service. However, current reliability evaluation approaches lack a formal semantic representation and the support of incomplete or uncertain information. We propose a Web service reliability ontology (WSRO) serving as a basis to characterize the knowledge of Web service. And based on WSRO, a mapping to the probability graphical model is constructed. The Web service reliability evaluation results are obtained by the causality reasoning. Some evaluation results reveal that our approach is applicable and effective.

The basic idea of H 2 -C mixture reduction reflexes using hydrogen as main reductant and carbon as main heat generator in iron bath smelt reduction reactors on purpose to cut down total energy consumption and CO 2 emission, protect the environment and take advantage of the new energy source. First of all, the calculation of mass and energy balance is very important for the smelt production, optimization of smelt proceeding and computer automation control. The author of this paper applied the methods of modeling for mass balance and energy balance after brief description of the reactor to research the balance behavior of this new metallurgical reactor. The balance model is compiled by Visual B include brief control panel and flex changeable parameter which can be used to simulate the different new smelt proceedings according to the input parameters and the changes of the material compositions. The work of this paper is one of bases for further kinetics model of iron bath reactor with H 2 -C mixture reduction in way of research and development.

This paper presents an all-digital 4-bit delta-sigma (ΔΣ) modulator for envelope elimination and restoration (EER)-based polar transmitters. A fast feedback approach is devised by combining the digital truncator with path gains to reduce the propagation delay of feedback loops in modulators. The CMOS standard cell-based design could hence be utilized to implement the proposed modulator at a sampling frequency of 182 MHz. The noise transfer function of the presented ΔΣ modulator has been optimized to obtain a maximally flat noise band to easily meet the EDGE spectrum mask. Experiment results show the presented ΔΣ modulator has the noise power beneath −60 dB below the full-scale EDGE signal within ±20 MHz of the carrier frequency. The measured adjacent channel power ratios and alternate channel power ratio of the proposed modulator also give a 6 dB margin to the EDGE specification at 400 kHz and 600 kHz offsets.

N-nitrosodimethylamine (NDMA) is carcinogenic. To estimate current risk of NDMA to human health in a given area, Level III multimedia fugacity model has been used to simulate the fate and risk of NDMA in environment. The calculated NDMA concentration in water which is strongly related to the disinfection by chlorination agrees well with that measured. The biggest bulk sink of NDMA is water, which accounts for 99.4% of total amount of NDMA in the environment. Transporting from air to water is the major intermedia transfer processe, while reaction and advection in water is the main pathway for NDMA disappearing in environment.

We present a new computational method for predicting ligand binding sites in protein sequences. The method uses kernelbased canonical correlation analysis and linear regression to identify binding sites in protein sequences as the residues that exhibit strong correlation between the residues' evolutionary characterization at the sites and the structure based functional classification of the proteins in the context of a functional family. We explore the effect of correlations among multiple positions in the sequences and show that their inclusion enhances the prediction accuracy significantly.

In this paper, multiresolution data visualiztion techniques are discussed, which include auto-levelswitch between datasets of different resolution, rendering with adaptive resolution and interactive visualization methods for AMR data (Adaptive Mesh Refinement Data). Some key techniques about implemention are summarized in the paper. Observer pattern is used for message passing basing on interfaces of attribute subjects. It leads to update of visualization window and level-switch of different resolution in auto mode. Given threshold appropriate resolution level and relative data blocks are choosed on specified strategy, that high resolution is adopted on data regions varying intensively and low for contrary. Three choices such as rendering whole or partial data on single level or partial data for multi levels are designed for interactive mode. We demonstrate the efficiency and robustness of the interactive visualization techniques with some real physical datasets.

Protein functional site prediction plays a key role in understanding protein function and in protein engineering. In this work we developed a novel method using canonical correlation analysis to predict protein ligand binding sites. The method was tested with a well-known benchmark dataset and consistently outperformed the existing method Xdet, which is based on Pearson correlation, by improving the lowest and highest ranked positives for more than 18% and 22% respectively.

Microwave Tube Simulator Suite (MTSS)1 is a full-featured, tightly integrated software package for microwave tube analysis and design. It includes a friendly user interface module and three physics simulators. Microwave Tube Design Environment (MTDE) is the user interface module, which provides a powerful solid modeling front-end and graphical and computational post-processing functionality. Electron Optics Simulator (EOS)2 is a 2D/3D finite-element design tool for microwave tube electron guns and collectors. High Frequency Circuit Simulator (HFCS)3 is an advanced, three-dimensional electromagnetic simulation tool for RF components. Beam Wave Interaction Simulator (BWIS) is a design tool for the electron beam and electromagnetic wave interaction. Researchers and designers can use MTSS to model and simulate the entire microwave tube, including solid modeling, simulation of all components and post-processing.

Using Beam Wave Interaction Simulator (BWIS), one module of Microwave Tube Simulator Suite (MTSS), a wideband helix TWT has been accurately simulated. The slow-wave circuit parameters are calculated by High Frequency Circuit Simulator.

Recent developments to the Electron Optics Simulator (EOS), an electron gun and collector design tool, are reported in this present. The code has been formally released at 2007, and there are some modifications and new methods to improve the code up to now.

Using HFCS-I and MAFIA, the impedance characteristics of a typical helical slow-wave structure (SWS) is studied, in which the Non-Resonant Perturbation (NRP) Method is adopted and the direct calculation from discrete Electromagnetic (EM) fields is performed. The results from HFCS-I and MAFIA are found to be consistent.

Microwave Tube Simulator Suite (MTSS) is a full-featured software package for microwave tube analysis and design. It has been developed by University of Electronic Science and Technology of China (UESTC) and validated against some helix traveling wave tubes.

The new version HFCS-E has capability to model SWS with dielectric loss rods. Some helix SWSs are simulated and results show that dielectric loss will cause resonance frequency a small falling, but not obvious difference of other parameters of helical SWS.

In this work, we developed a novel method to generate comparison trees based on characteristics collected from metabolic networks of bacteria. We characterize each bacterial genome’s metabolism by the occurrence frequencies of various chemical reactions classified by enzyme commission numbers, and by the correlation of the reaction types for any two consecutive reactions in pathways present in the networks. In hypothesizing that species physiologically close to each other should show high similarity in these characteristics, we quantitatively measure the similarity using Pearson correlation coefficient, and build comparison trees using the Neighbor-Joining algorithm. These Metabolic Characteristics (MC) based comparison trees cluster the bacteria according to their functional groups and reveal the relationship between different organisms from a physiological perspective yielding new insights about the organisms.

In China, tobacco is a state-run monopoly and facing more fierece competition after entry to WTO. In the paper, a model of cigarette supply chain which is composed of the Tobacco Monopoly Administration and one cigarette retailer is considered. The Tobacco Monopoly Administration acts as the leader. There are two kinds of substitute cigarette products sold in the market. The two kinds of cigarette products have different price elasticity of demand. The optimal order quantity of each cigarette product of the cigarette supply chain with wholesale price-only contract in the decentralized case by the retailer is half that of the cigarette supply chain in the centralized case. Also, the optimal unit retail price of each cigarette product in the decentralized case is higher than that in the centralized case. The total profit of the cigarette supply chain with wholesale price only contract in the decentralized case is less than half of the optimal profit in the centralized case. To achieve the optimal profit of the centralized cigarette supply chain in the decentralized case, a two-part tariff contract is proposed to coordinate the cigarette supply chain. The results are helpful to enhance the competibility of the cigarette supply chain in China.

Microwave tube simulator suite (MTSS) is a fully featured software package for microwave tube analysis and design. it consists of four modules now, microwave tube design environment (MTDE), electron optics simulator (EOS), high frequency circuit simulator (HFCS), and beam-wave interaction simulator (BWIS).

This paper provides the design and realization of high frequency circuit simulator (HFCS) which is based on the finite element method. For vilification, the cold-test characteristics of two actual helical slow wave structures have been calculated and the results were found to be consistent with those of HFSS simulation with same mesh density.

This paper provides the design and realization of high frequency circuit simulator (HFCS) which is based on the Finite Integration Technology. For vilification, the cold-test characteristics of two actual helical slow wave structures have been calculated and the results were found to be consistent with those of MAFIA simulation with same mesh density.

Electron optics designer (EOS) (Tao Huang et al., 2007) is a fully three-dimensional (3D) finite- element (FE) steady- state trajectory code, which is a solver of electrostatic field and trajectory that takes into account secondary electron, space-charge effect and external magnetic field. To validate the code, this paper illustrated two typical experimental verifications of electron gun and collector.