Visual Execution Analysis for Multiagent Systems

Multiagent systems have become increasingly important in developing complex software systems. Multiagent systems introduce collective intelligence and provide benefits such as flexibility, scalability, decentralization, and increased reliability. A software agent is a high-level software abstraction that is capable of performing given tasks in an environment without human intervention. Although multiagent systems provide a convenient and powerful way to organize complex software systems, developing such system is very complicated. To help manage this complexity this research develops a methodology and technique for analyzing, monitoring and troubleshooting multiagent systems execution. This is accomplished by visualizing a multiagent system at multiple levels of abstraction to capture the relationships and dependencies among the agents

Systems Features Analysis (SFA) and Analytic Hierarchy Process (AHP) in Systems Design and Development

This paper tries to address the problem of deriving the different features of a system and then having a way of making informed decisions about them based on their level of importance to the whole system as well as to each other depending on several given factors. The use of Systems Features Analysis (SFA) to derive the features and Applied Hierarchy Process (AHP) to decide on their importance fits the given situation and they are described in this paper. These tools are successfully applied to two system development cases, a whole system and some components of a system respectively, which showed their effectiveness and usefulness. An AHP-based software called SuperDecisions is utilized to immediately use AHP in the software design and development process in the shortest possible time

Neural node network and model, and method of teaching same

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing

Navigation System Heading and Position Accuracy Improvement through GPS and INS Data Fusion

Commercial navigation systems currently in use have reduced position and heading error but are usually quite expensive. It is proposed that extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) be used in the integration of a global positioning system (GPS) with an inertial navigation system (INS). GPS and INS individually exhibit large errors but they do complement each other by maximizing the advantage of each in calculating the heading angle and position through EKF and UKF. The proposed method was tested using low cost GPS, a cheap electronic compass (EC), and an inertial management unit (IMU) which provided accurate heading and position information, verifying the efficacy of the proposed algorithm

Trajectory Tracking and Stabilization of a Quadrotor Using Model Predictive Control of Laguerre Functions

This paper presents a solution to stability and trajectory tracking of a quadrotor system using a model predictive controller designed using a type of orthonormal functions called Laguerre functions. A linear model of the quadrotor is derived and used. To check the performance of the controller we compare it with a linear quadratic regulator and a more traditional linear state space MPC. Simulations for trajectory tracking and stability are performed in MATLAB and results provided in this paper

DeePromoter: Robust Promoter Predictor Using Deep Learning

The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm

Design of feedforward and feedback position control for passive bilateral teleoperation with delays

Bilateral teleoperation systems connected to computer networks such as the internet must be able to operate with varying time delays since such systems can easily become unstable. A passivity concept has been used as the framework to solve the stability problem in the bilateral control of teleoperation systems. Passivity and tracking performance are recovered using a control architecture that incorporates time varying gains into the transmission path, feedforward, and feedback position control. The proposed architecture has an inner component that can accommodate any configuration but still remain stable and passive even with varying time delay. The simulation results for a single degree of freedom master/slave system demonstrate the performance of the proposed control architecture