• Journal of KIISE:Databases
• /
• v.37 no.6
• /
• pp.324-332
• /
• 2010

The majority of event detection in real-time embedded sensor network systems is based on data fusion that uses noisy sensor data collected from complicated real-world environments. Current research has produced several excellent low-level mechanisms to collect sensor data and perform aggregation. However, solutions that enable these systems to provide real-time data processing using readings from heterogeneous sensors and subsequently detect complex events of interest in real-time fashion need further research. We are developing real-time event detection approaches which allow light-weight data fusion and do not require significant computing resources. Underlying the event detection framework is a collection of real-time monitoring and fusion mechanisms that are invoked upon the arrival of sensor data. The combination of these mechanisms and the framework has the potential to significantly improve the timeliness and reduce the resource requirements of embedded sensor networks. In addition to that, we discuss about a privacy that is foundation technique for trusted embedded sensor network system and explain anonymization technique to ensure privacy.

• Journal of Communications and Networks
• /
• v.15 no.1
• /
• pp.87-101
• /
• 2013

In industrial applications, sensor networks have to satisfy specified time requirements of exchanged messages. IEEE 802.15.4 defines the communication protocol of the physical and medium access control layers for wireless sensor networks, which support real-time transmission through guaranteed time slots (GTSs). In order to improve the performance of IEEE 802.15.4 in industrial applications, this paper proposes a new traffic scheduling algorithm for GTS. This algorithm concentrates on time-critical industrial periodic messages and determines the values of network and node parameters for GTS. It guarantees real-time requirements of periodic messages for industrial automation systems up to the order of tens to hundreds of milliseconds depending on the traffic condition of the network system. A series of simulation results are obtained to examine the validity of the scheduling algorithm proposed in this study. The simulation results show that this scheduling algorithm not only guarantees real-time requirements for periodic message but also improves the scalability, bandwidth utilization, and energy efficiency of the network with a slight modification of the existing IEEE 802.15.4 protocol.

• Journal of the Semiconductor & Display Technology
• /
• v.23 no.2
• /
• pp.19-22
• /
• 2024

EtherCAT is an Ethernet-based fieldbus system standardized in IEC 61158 and SEMI, and widely used in the fields of factory automation, semiconductor equipment and robotics. In this paper, we summarize the current status of Xenomai real-time framework and RTnet, which are essential for Linux operating systems to operate in real-time, and implement a real-time EtherCAT master system with these open sources. The real-time performance of the implemented EtherCAT master is evaluated according to Intel network controllers 82574L, I219, I210, and I225, respectively. The results show that the implemented EtherCAT master provides precise control performance for control frequencies from 1KHz to 8KHz and similar performance for I219, I210, and I225, and relatively slightly larger jitter for 82574L.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.19-27
• /
• 2021

Tasks scheduling have been gaining attention in both industry and research. The scheduling that ensures independent task execution is critical in real-time systems. While task scheduling has gained a lot of attention in recent years, there have been few works that have been implemented into real-time architecture. The efficiency of the classical scheduling strategy in real-time systems, in particular, is still understudied. To reduce total waiting time, we apply three scheduling approaches in this paper: First In/First Out (FIFO), Shortest Execution Time (SET), and Shortest-Longest Execution Time (SLET). Experimental results have demonstrated the efficacy of the SLET in comparison with the others in most cases in a wide range of configurations.

• Nuclear Engineering and Technology
• /
• v.51 no.3
• /
• pp.702-708
• /
• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.175-177
• /
• 2006

This paper deals with implementation of middleware using CAN(Controller Area Network) network and TCP/IP for real-time distributed control system of a humanoid robot. Existent system using CAN network is available. But, there is problems in extensibility and flexibility. In this raper, the new system using TCP/IP for solution and improvement of problems is proposed. The new system is applied to ISHURO-II, real-humanoid robot. The performance is verified through experiment.

• Journal of Korea Multimedia Society
• /
• v.23 no.10
• /
• pp.1236-1249
• /
• 2020

In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.7
• /
• pp.602-610
• /
• 2000

In communication networks used in safety-critical systems such as control systems in nuclear power plants there exist three types of data traffic : urgent or asynchronous hard real-time data hard real-time periodic data and soft real-time periodic data. it is necessary to allocate a suitable bandwidth to each data traffic in order to meet their real-time constraints. This paper proposes a method to meet the real-time constraints for the three types of data traffic simultaneously under a timer-controlled token bus protocol or the IEEE 802.4 token bus protocol and verifies the validity of the presented method by an example. This paper derives the proper region of the high priority token hold time and the target token rotation time for each station within which the real-time constraints for the three types of data traffic are met, Since the scheduling of the data traffic may reduce the possibility of the abrupt increase of the network load this paper proposes a brief heuristic method to make a scheduling table to satisfy their real-time constraints.

• Journal of Energy Engineering
• /
• v.14 no.2 s.42
• /
• pp.112-116
• /
• 2005

This paper describes the implementation of DCS communication network that provides high bandwidth and reliability. The network for DCS in this paper adopts the Reflective Memory (RM) architecture and Fast Ethernet physical media that have 100 Mbps bandwidth. Also, This network uses Ring Enhancement Device (RED) which is invented to reduce the time delay of each node. The DCS network that is introduced in this paper is named as ERCNet (Ethernet based Real-time Control Network). This paper describes the architecture and working algorithms of ERCNet and performs numerical analysis. In addition, the performance of ERCNet is evaluated by experiment using the developed ERCNet network.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2004.04a
• /
• pp.253-257
• /
• 2004

Neural networks are works are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division(corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.