• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.907-913
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• 1999

For an automated transportation system like PRT(Personal Rapid Transit) system or IVHS, an efficient vehicle-merging algorithm is required for smooth operation of the network. For management of merging, collision avoidance between vehicles, ride comfort, and the effect on traffic should be considered. This paper proposes an unmanned vehicle-merging algorithm that consists of two procedures. First, a longitudinal control algorithm is designed to keep a safe headway between vehicles in a single lane. Secondly, 'vacant slot and ghost vehicle' concept is introduced and a decision algorithm is designed to determine the sequence of vehicles entering a converging section considering energy consumption, ride comfort, and total traffic flow. The sequencing algorithm is based on fuzzy rules and the membership functions are determined first by an intuitive method and then trained by a learning method using a neural network. The vehicle-merging algorithm is shown to be effective through simulations based on a PRT model.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.968-973
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• 2000

Fault diagnosis plays an important role in the performance and safe operation of many modern engineering plants. This paper investigates the problem of fault detection using neural networks in dynamic systems. A general framework for constructing a nonlinear fault detection scheme for nonlinear dynamic systems containing modeling uncertaintly is proposed. The main idea behind the proposed approach is to monitor the physical system with an off -line learning neural network and then to approximate the upper and lower thresholds of acceleration of the nominal system with the model-based threshold(ThMB) method, The performance of the proposed fault detection scheme is investigated through simulations of a pendulum with uncertainty.

• Structural Engineering and Mechanics
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• v.23 no.2
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• pp.177-193
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• 2006

Determining the hysteretic energy demand and dissipation capacity and level of damage of the structure to a predefined earthquake ground motion is a highly non-linear problem and is one of the questions involved in predicting the structure's response for low-performance levels (life safe, near collapse, collapse) in performance-based earthquake resistant design. Neural Network (NN) analysis offers an alternative approach for investigation of non-linear relationships in engineering problems. The results of NN yield a more realistic and accurate prediction. A NN model can help the engineer to predict the seismic performance of the structure and to design the structural elements, even when there is not adequate information at the early stages of the design process. The principal aim of this study is to develop and test multi-layered feedforward NNs trained with the back-propagation algorithm to model the non-linear relationship between the structural and ground motion parameters and the hysteretic energy demand in steel moment resisting frames. The approach adapted in this study was shown to be capable of providing accurate estimates of hysteretic energy demand by using the six design parameters.

• International Journal of Safety
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• v.5 no.2
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• pp.34-37
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• 2006

As part of an effort to systematize the operation planning for cutting processes, the neural network method has been applied to model the process of selecting cutting conditions and subsequently to arrive at effective and safe cutting conditions through learning during training of the model. New cutting conditions that are more effective and safer for the given circumstance are obtained. The proposed algorithm deletes the old information previously learned, and then makes the network make at improvement by learning. As a result, the new algorithm provides useful cutting conditions for safer manufacturing environments. A variety of simulation cases illustrate the performance of the proposed methodology. The simulation results are provided and discussed.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.4
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• pp.363-374
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• 2020

Fingerprint authentication identifies a user based on the individual's unique fingerprint features. Fingerprint authentication methods are used in various real-life devices because they are convenient and safe and there is no risk of leakage, loss, or oblivion. However, fingerprint authentication methods are often ineffective when there is contamination of the given image through wet, dirty, dry, or wounded fingers. In this paper, a method is proposed to remove noise from fingerprint images using a convolutional neural network. The proposed model was verified using the dataset from the ChaLearn LAP Inpainting Competition Track 3-Fingerprint Denoising and Inpainting, ECCV 2018. It was demonstrated that the model proposed in this paper obtains better results with respect to the methods that achieved high performances in the competition.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.11-16
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• 2023

Vehicular networks are part of the next generation wireless and smart Intelligent Transportation Systems (ITS). In the future, autonomous vehicles will be an integral part of ITS and will provide safe and reliable traveling features to the users. The reliability and security of data transmission in vehicular networks has been a challenging task. To manage data transmission in vehicular networks, road networks are divided into clusters and a cluster head is selected to handle the data. The selection of cluster heads is a challenge as vehicles are mobile and their connectivity is dynamically changing. In this paper, a novel secure cluster head selection algorithm is proposed for secure and reliable data sharing. The idea is to use the secrecy rate of each vehicle in the cluster and adaptively select the most secure vehicle as the cluster head. Simulation results show that the proposed scheme improves the reliability and security of the transmission significantly.

• ETRI Journal
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• v.45 no.2
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• pp.329-337
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• 2023

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.53-58
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• 2024

The Internet of Things (IoT) enables the connection of millions of disparate devices to the World Wide Web. To perform the task, a lot of smart gadgets must work together. The gadgets recognize other devices as part of their network service. Keeping participating devices safe is a crucial component of the internet of things. When gadgets communicate with one another, they require a promise of confidence. Trust provides certainty that the gadgets or objects will function as expected. Trust management is more difficult than security management. This review includes a thorough examination of trust management in a variety of situations.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.219-227
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• 2014

Ethernet, ATM, and CAN are wide-utilized communication protocols for information transfer by internet. Many researches about Network Time-delay have been based on network modeling. But almost of them have not shown an optimal solution in various communication environments. So, asynchronous sample system modeling based internet is needed to be robust in various network environments. Also as closed loop system in internet has a different operational characteristics and noise characteristics comparing with conventional control system, new robust control method is needed in instruments which demand to be safe and precise for internet environments. In order to achieve the safe and precise real-time control in remote plant, this paper is aimed to analysis the transfer characteristics and time-delay of control system in cluster web server.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.37-48
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• 2004

Important Java network application services have been rapidly increased along with the growth of the Internet. So, it is desirable for such applications to serve transparently, continuously and safely even if the network is temporally partitioned or certain hosts running those services are crashed down. To satisfy such requirements, many group communication systems have been developed. However, existing Java-based group communication systems do not support both the extended virtual synchrony and various types of message delivery such as FIFO, causal, total and safe delivery service. In this paper, we present the design and implementation of a Java group communication system, named JACE, supporting various types of message delivery between group members and the extended virtual synchrony model. The JACE system consists of a number of protocol modules which can be stacked on top of each other in a variety of ways. In addition, using the JACE system, we have developed an experimental UDDI registry for discovering and publishing information about Web services.