Behavior Modeling Method Research Articles

A time-domain boundary element method for quasistatic thermoviscoelastic behavior modeling of the functionally graded materials

In the present paper, a boundary element formulation is presented for two-dimensional thermoviscoelasticity analysis of components fabricated from the functionally graded materials (FGMs). In this regard, a graded viscoelastic element capable of tracing gradual variations of the material properties is developed. Several attempts have been made so far to employ the integral equation approach for the heterogeneous viscoelastic materials. In the present research, Somigliana’s displacement identity is considered and implemented numerically for analyzing the two-dimensional exponentially graded viscoelastic components. Employing the common assumptions of the viscoelastic constitutive equations and the weighted residual technique, an efficient boundary element formulation is developed for the heterogeneous Kelvin–Voigt solid viscoelastic models. Finally, three numerical examples are provided to verify the proposed formulation and present practical conclusions.

Behavioral Modeling and Design of Predistortion for Wireless Transmitters with Multi-Branch RF Power Amplifier

In this paper, behavioral modeling approach and predistortion design methods are proposed for wireless transmitters with multi-branch radio frequency (RF) power amplifiers (PAs). Multi -branch RF PA architectures are studied such as balanced RF PA systems and linear amplification with nonlinear component (LINC) systems. Conventional behavioral modeling using single input and single output data cannot extract the behaviors of RF PAs in each branch. The behavioral modeling method using separated input signals and weighted input identification is proposed to extract accurate behavioral models for each branch. A decentralized predistortion structure that consists of multi-branch memory polynomial architecture predistortion components is proposed. The proposed behavioral modeling method is used for the design of decentralized predistortion using indirect learning method. The performances of proposed modeling and predistortion method are evaluated by comparing the normalized mean square error (NMSE) values with conventional single-branch behavioral modeling and predistortion. The input signal is 10MHz long-term evolution (LTE) signal. The results show that the proposed behavioral modeling and decentralized predistortion architecture are suitable for wireless transmitters with multi-branch RF PAs.

An Effective User Behavior Modeling Approach for Data Services in the Field of Materials Engineering

New challenges about data services have arisen, especially consider the impact of user behavior. For dealing with the problem of distributed heterogeneous data sharing and satisfying the demands of data service, the complex association and dynamic changes should be tracked timely. Thereby it has become increasingly important to build a data services framework based on the user behavior analysis. In this paper, we propose an effective user behavior modeling approach based on the Open Cloud Service Architecture (OCSA) to manage the domain scientific data. Firstly, we build the data services framework in the cloud environment, and describe the related theories and conceptual model about this framework. Based on this, we elaborate the definition and classification of user behavior. Secondly, we construct the User Behavior Model (UBM) by tracking the user operation behavior respectively from the time dimension and the space dimension. Finally, we designed and realized a behavior-oriented Materials Scientific Data Sharing Service Platform. Through the description and comparison of application case, the validity of user behavior modeling method is verified.

Behavioral Modeling with Node Matching Algorithm for Conceptual Design

Computer-aided conceptual design of mechatronic products is becoming more and more important since it offers much convenience in design scheme retrieval and enhances the utilization of existing designs. One of the key technologies for computer-aided conceptual design is an efficient search algorithm for retrieving existing designs. As such, it’s necessary to find a feasible way to select the most suitable design out of a number of candidates for further development. By applying behavioral modeling method for representation of conceptual design, this paper proposes a node matching algorithm for searching behavioral model, together with a model similarity evaluation method. Based on these methods, a framework for computer-aided conceptual design is subsequently proposed. Finally, a case study is presented to illustrate the applicability of the proposed methodologies.

The Development of Motivation to Lead and Leader Role Identity

Leadership development has been continually recognized by organizations as a competence and a mind-set that is worthy of substantial time and resources. Although various approaches have stressed the development of skills or behaviors, it is becoming increasingly apparent that the development of attitudes and belief systems is also relevant. In an effort to increase knowledge in this area, the authors conducted a quasi-experimental study to understand whether motivation to lead (social-normative) and leader role identity would increase in business students exposed to behavior-modeling methods emphasizing the importance of transformational leadership. The results show that, in comparison with students in an introduction to management course, the quasi-experimental–condition students significantly increased the value that they placed on the leader role, as well as their identity as leaders. The findings are discussed in terms of both theoretical and practical implications.

A new cluster model based descriptor for structure-inhibition relationships: A study of the effects of benzimidazole, aniline and their derivatives on iron corrosion

Interaction energies of benzimidazole, aniline and nine of their derivatives on the surface of iron are calculated in hydrochloric acid medium via cluster model by using quantum chemical calculations at DFT level. The interaction energy is then used as a descriptor in a QSIR method for corrosion behavior modeling of the inhibitors in solutions at different concentrations. It is shown that, a combination of interaction energy and three descriptors related to polarizability, electronegativity and 3D structure of inhibitor molecules can excellently describe the corrosion inhibition on iron. The findings are indicative of the effectiveness of the new descriptor.

Research on Behavior Modeling Method of Agent-Based CGF

This paper studies the CGF behavior modeling method in Agent-based TBM combat simulation system, which focuses on the decision-making behavior and combat behavior of CGF Agent. Agent entities are divided into three levels, and a three-level organizational structure of Agent has been established. Use Rough Set theory to treat the uncertainty information of decision-making. To reflect the relationship between attack and defense, we establish a BDI model based on rival model to track the status of rival Agent and do real-time response. Finally gives the realization method of TBM combat simulation system.

BEHAVIORAL MODEL OF SYMMETRICAL MULTI-LEVEL T-TREE INTERCONNECTS

An accurate and behavioral modeling method of symmet- rical T-tree interconnect network is successfully investigated in this paper. The T-tree network topology understudy is consisted of ele- mentary lumped L-cells formed by series impedance and parallel ad- mittance. It is demonstrated how the input-output signal paths of this single input multiple output (SIMO) tree network can be reduced to single input single output (SISO) network composed of L-cells in cascade. The literal expressions of the currents, the input impedances and the voltage transfer function of the T-tree electrical interconnect via elementary transfer matrix products are determined. Thus, the exact expression of the multi-level behavioral T-tree transfer function is established. The routine algorithm developed was implemented in Matlab programs. As application of the developed modeling method, the analysis of T-tree topology comprised of difierent and identical RLC-cells is conducted. To demonstrate the relevance of the model established, lumped RLC T-tree networks with difierent levels for the microelectronic interconnect application are designed and simulated. The work ∞ow illustrating the guideline for the application of the rou- tine algorithm summarizing the modeling method is proposed. Then, 3D-microstrip T-tree interconnects with width 0.1m and length 3mm printed on FR4-substrate were considered. As results, a very good agreement between the results from the reduced behavioral model pro- posed and SPICE-computations is found both in frequency- and time- domains by considering arbitrary binary sequence \01001100 with 2Gsym/s rate. The model proposed in this paper presents signiflcant beneflts in terms of ∞exibility and very less computation times. It can be used during the design process of the PCB and the microelectronic circuits for the signal integrity prediction. In the continuation of this

A Study on Network Service Behavior Verification with Process Algebra and Its Application

With formalization and modeling system,we can analyze system characteristics,verify system behavior and try to avoid conflicts through.This paper presents a behavior modeling method for network services components using process algebra,including strong simulation,strong bisimulation,timeliness,service triggers,and service content.Then the P2P discovery of nodes and firewall traversal behavior description are presented.And a network self-management protocol named RCMMS is proposed with process algebra description to verify the proposed method.

Behavior Modeling Method of CGF in TBM Combat Simulation

This paper studies the CGF behavior modeling method in Agent-based TBM combat simulation system, which focuses on the decision-making behavior and combat behavior of CGF Agent. Agent entities are divided into three levels, and a three-level organizational structure of Agent has been established. To reflect the relationship between attack and defense, we establish a BDI model based on rival model to track the status of rival Agent and do real-time response. Finally gives the realization method of TBM combat simulation system.

11.3.3 Behavioral Models and Improved Quality in Architecture‐Centric Engineering

AbstractArchitecture‐Centric Engineering (ACE) is described by the authors in 'Achieving Interoperability for Critical IT and Communication Systems' (Desourdis, et al, 2009) as an engineering approach that derives requirements and performance targets from enterprise scenarios. As part of this approach, this paper summarizes the use of behavioral models for improved quality as a repeatable, integrated component of systems engineering. Predictable quality measurement is possible at transitions earlier in the engineering lifecycle. Case Study, behavior model analytics and summaries from expert peer reviews are presented to support this quality improvement. An approach for the integration of behavior modeling methods as a quality mechanism extension is summarized.

状況と行動の因果関係に着目した人間の行動モデル化手法

Various kinds of sophisticated systems are utilized in our daily life, therefore these systems must have the ability to work considering behaviors of humans around them. In order to realize such a system, a kind of human behavior modeling method is required. In this paper, we propose a new modeling method of human behaviors based on the causality between the situation around a person and the human behavior. In this method, we suppose that a person changes his behavior according to the change of the situation around him. In this paper, a human behavior is discribed by a discrete event and the situation around the person is described by multidimensional time series signal. The causality between the time series data of the situation and the discrete event of the human behavior is defined as behavior rule. In this rule, the multidimensional time series data is modeled by HMM(Hidden Markov Model) in order to consider its temporal and spatial redundancy. Various kinds of human behavior rules are acquired based on the combination between the situation around a person and his behavior. Therefore, a set of these human behavior rules is called as a human behavior model in this paper. In the experiment, the controller operation of a radio controlled vehicle is modeled by the proposed method. The usefulness of the proposed method is examined through some experimental results with the estimation system of the next human behavior based on the generated behavior model.

Transient Behavioral Modeling of Nonlinear I/O Drivers Combining Neural Networks and Equivalent Circuits

In this letter, a new method for nonlinear behavioral modeling of high-speed I/O drivers is presented, combining neural networks with driver specific circuit knowledge. In the proposed technique, the circuit knowledge of the driver is exploited to preserve the physical property of the driver. In addition, several neural network sub-models are incorporated into the overall model structure to effectively compensate the missing information in the existing buffer models, when dealing with analog input signals of various shapes. The validity and efficiency of the proposed technique are demonstrated through the modeling of a commercial I/O driver and the use of the resulting model for signal integrity simulations.

Abnormality detection using low-level co-occurring events

We propose in this paper a method for behavior modeling and abnormal events detection which uses low-level features. In conventional object-based approaches, objects are identified, classified, and tracked to locate those with suspicious behavior. We proceed directly with event characterization and behavior modeling using low-level features. We first learn statistics about co-occurring events in a spatio-temporal volume in order to build the normal behavior model, called the co-occurrence matrix. The notion of co-occurring events is defined using mutual information between motion labels sequences. Then, in the second phase, the co-occurrence matrix is used as a potential function in a Markov random field framework to describe, as the video streams in, the probability of observing new volumes of activity. The co-occurrence matrix is thus used for detecting moving objects whose behavior differs from the ones observed during the training phase. Interestingly, the Markov random field distribution implicitly accounts for speed, direction, as well as the average size of the objects without any higher-level intervention. Furthermore, when the spatio-temporal volume is sufficiently large, the co-occurrence distribution contains the average normal path followed by moving objects. Our method has been tested on various indoor and outdoor videos representing various challenges.

Formal behavior modeling and effective automatic refinement

Modeling and refining behaviors of software systems are two crucial issues in the methodology of Model-Driven Development (MDD). Traditional methods include Unified Modeling Language (UML) based methods and formal methods. Recently integrated methods by taking full advantages of these two methods have received increasing attention. Unfortunately, there are still no effective formal modeling and automatic refinement methods for behaviors. Besides, difficulties exist in generating some structures, e.g., explicit loop structures, via existing approaches. In this paper, we propose a B-notations based framework for formal modeling and automatically refining behaviors. In our framework, three issues are involved: automatic behavior modeling, theory of behavior refinement, and automatic behavior refinement. For automatic behavior modeling, we propose a B-notation based automatic modeling method for behaviors. For theory of behavior refinement, we propose a refinement theory focusing on behaviors that regards the behavior refinement problem as a search problem. For automatic behavior refinement, we propose an automatic refinement approach by combining top-down logic reduction based method and bottom–up local search based method, where explicit loop structures can be well generated.

High-Dimensional Neural-Network Technique and Applications to Microwave Filter Modeling

Neural networks are useful for developing fast and accurate parametric model of electromagnetic (EM) structures. However, existing neural-network techniques are not suitable for developing models that have many input variables because data generation and model training become too expensive. In this paper, we propose an efficient neural-network method for EM behavior modeling of microwave filters that have many input variables. The decomposition approach is used to simplify the overall high-dimensional neural-network modeling problem into a set of low-dimensional sub-neural-network problems. By incorporating the knowledge of filter decomposition with neural-network decomposition, we formulate a set of neural-network submodels to learn filter subproblems. A new method to combine the submodels with a filter empirical/equivalent model is developed. An additional neural-network mapping model is formulated with the neural-network submodels and empirical/equivalent model to produce the final overall filter model. An H -plane waveguide filter model and a side-coupled circular waveguide dual-mode filter model are developed using the proposed method. The result shows that with a limited amount of data, the proposed method can produce a much more accurate high-dimensional model compared to the conventional neural-network method and the resulting model is much faster than an EM model.

Discriminative Data Visualization for Daily Behavior Modeling

This paper describes a novel data visualization technique for intuitive interpretation of human behavior modeling results. Our visualization method captures the relational structure of clusters obtained by some human behavior modeling method, and reflects it to two- to three-dimensional space. The main advantage of our method is that the method can be applied to massive daily sensor log analysis. This paper reports some experimental results on pyroelectric sensor log analysis where the data spans months to years.

Symbolic Analysis of Analog Circuits By Boolean Logic Operations

In this brief, the author proposes a novel symbolic analysis method for analog behavioral modeling by Boolean logic operations and graph representation. The exact symbolic analysis problem is formulated as a logic circuit synthesis problem where we build a logic circuit that detects whether or not a given symbolic term is a valid product term from a determinant. The logic circuit is represented by binary decision diagrams (BDDs), which can be trivially transformed into zero-suppressed BDDs (ZBDDs). ZBDDs are essentially determinant decision diagram (DDD) representation of a determinant. The proposed BBD-based method gives the circuit logic interpretation of symbolic terms in a determinant and exploits such logic interpretation during the BDD/DDD construction process. It demonstrates an inherent relationship between symbolic circuit analysis and logic synthesis. It is the first symbolic analysis method that is not based on traditional Laplace expansion or topological methods. Experimental results show the speedup of our new method over the existing flat method and its improved analysis capacity over both existing flat and hierarchical symbolic analyzers

Modeling events as entities in object-oriented conceptual modeling languages

Most current conceptual modeling languages and methods do not model events as entities. We argue that, at least in object-oriented (O-O) languages, modeling events as entities provides substantial benefits. We show that a method for behavioral modeling that deals with event and entity types in a uniform way may yield better behavioral schemas. The proposed method makes extensive use of language constructs such as constraints, derived types, derivation rules, type specializations and operations, which are present in all complete O-O conceptual modeling languages. The method can be adapted to most O-O languages. In this paper we explain its adaptation to the UML.

Philosophy regarding the design of production systems : Peter Jacobsen, Leif Flemming Pedersen, Povl Erik Jensen, Claus Witfelt, v20, n6, 2001, pp405–415

We propose in this paper a method for behavior modeling and abnormal events detection which uses low-level features. In conventional object-based approaches, objects are identified, classified, and tracked to locate those with suspicious behavior. We proceed directly with event characterization and behavior modeling using low-level features. We first learn statistics about co-occurring events in a spatio-temporal volume in order to build the normal behavior model, called the co-occurrence matrix. The notion of co-occurring events is defined using mutual information between motion labels sequences. Then, in the second phase, the co-occurrence matrix is used as a potential function in a Markov random field framework to describe, as the video streams in, the probability of observing new volumes of activity. The co-occurrence matrix is thus used for detecting moving objects whose behavior differs from the ones observed during the training phase. Interestingly, the Markov random field distribution implicitly accounts for speed, direction, as well as the average size of the objects without any higher-level intervention. Furthermore, when the spatio-temporal volume is sufficiently large, the co-occurrence distribution contains the average normal path followed by moving objects. Our method has been tested on various indoor and outdoor videos representing various challenges.