• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2019.11a
• /
• pp.107-108
• /
• 2019

In the past, regional pilot service is provided by individual pilots directly on board each ship, but it will be difficult to provide this type of service in the near future when autonomous vessels are activated. This can be seen as the need for automation and intelligence of pilot service and provision of remote control system. In this paper, we propose a method, system, and service target that can carry out the electronic pilot service through the intelligence aids to navigation for smart port operation.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2008.11a
• /
• pp.364-367
• /
• 2008

Earthwork is very equipment-intensive task and researches related to automated excavation have been conducted. There is an issue to secure the safety for an automated excavating system. Therefore, this paper focuses on how to improve safety for semi- or fully-automated backhoe excavation. The primary objective of this research is to develop object detection algorithm for automated safety system in excavation work. In order to satisfy the research objective, a diverse sensing technologies are investigated and analysed in terms of functions, durability, and reliability and verified its performance by several tests. The authors developed the objects detecting algorithm for user interface program using laser sensor. The results of this study would be the basis for developing the automated object detection system.

• Journal of Aerospace System Engineering
• /
• v.16 no.2
• /
• pp.13-24
• /
• 2022

As the scale of airborne system software increases, the use of OOT (Object-Oriented Technology) is increasing for functional expansion, efficient development, and code reuse, but the verification method for airborne object-oriented software is conducted from the perspective of the existing procedure-oriented program. The purpose of this paper was to analyze the characteristics of OOT and the vulnerabilities derived from the functional characteristics of OOT, and present a verification method applicable to each software development process (Design, Coding and Testing) to ensure the functional safety integrity of aviation software to which OOT is applied. Additionally, we analyzed the meaning of the static analysis results among the step-by-step verification measures proposed by applying LDRA, a static analysis automation tool, to PX4, an open source used to implement flight control software.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.3
• /
• pp.107-118
• /
• 2013

In this paper, we developed the semiconductor monitoring system for the etching process. Around the world, expert companies are competing fiercely since the semiconductor industry is a leading value-added industry that produces the essential components of electronic products. As a result, many researches have been conducted in order to improve the quality, productivity, and characteristics of semiconductor products. Process monitoring techniques has an important role to give an equivalent quality and productivity to produce semiconductor. In fact, since the etching process to form a semiconductor circuit causes great damage to the semiconductors, it is very necessary to develop a system for monitoring the process. The proposed monitoring system is mainly focused on the dry etching process using plasma and it provides the detailed observation, analysis and feedback to managers. It has the functionality of setting scenarios to match the process control automatically. In addition, it maximizes the efficiency of process automation. The result can be immediately reflected to the system since it performs real-time monitoring. UI (User Interface) provides managers with diagnosis of the current state in the process. The monitoring system has diverse functionalities to control the process according to the scenario written in advance, to stop the process efficiently and finally to increase production efficiency.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.12
• /
• pp.546-554
• /
• 2019

Typically, closed-circuit television (CCTV) monitoring is mainly used for post-processes (i.e. to provide evidence after an incident has occurred), but by using a streaming video feed, machine-based learning, and advanced image recognition techniques, current technology can be extended to respond to crimes or reports of missing persons in real time. The multi-CCTV cooperation technique developed in this study is a program model that delivers similarity information about a suspect (or moving object) extracted via CCTV at one location and sent to a monitoring agent to track the selected suspect or object when he, she, or it moves out of range to another CCTV camera. To improve the operating efficiency of local government CCTV control centers, we describe here the partial automation of a CCTV control system that currently relies upon monitoring by human agents. We envisage an integrated crime prevention service, which incorporates the cooperative CCTV network suggested in this study and that can easily be experienced by citizens in ways such as determining a precise individual location in real time and providing a crime prevention service linked to smartphones and/or crime prevention/safety information.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.3
• /
• pp.931-942
• /
• 2010

A home network provides users with a variety of information services. The kind and quality of the services can be substantially enhanced by utilizing a variety of data from sensors. However, home networks currently limit their potential by focusing on providing multimedia services rather than services utilizing sensor data. Outdoor electronics are frequently made in a form that emphasizes only certain limited functions in contrast to home appliances. Thus, sensors with one or two functions rather than many can be used in outdoor systems and their use will be more economical than using sensor nodes indoors with more complex home appliances. In this study, we chose to work with motion sensors as they have many potential uses, and we selected a parking lot control system with to use the motion sensors. This parking lot control system was implemented and applied as part of a home network. For this purpose, we defined and implemented a protocol to manage the network in a ubiquitous sensor network environment for the wireless home network in this study. Although a network management system in a Ubiquitous Sensor Network (USN) related to this study is being advanced for other projects, the protocol interface and message system have not yet been clearly defined for use in a general purpose network or in an extension into heterogeneous kinds of networks, communication support, etc. Therefore, USN network management should be conducted for management of faults, composition, power, and applications. To verify the performance of the protocol interface designed in this study, we designed and implemented the necessary units (sensor nodes, sensor gateway, and server) for each network section and, with them, proved the validity of this study.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.2
• /
• pp.183-194
• /
• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.4
• /
• pp.571-579
• /
• 2005

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.3
• /
• pp.339-350
• /
• 2003

In this paper, we propose a simultaneous multithreading (SMT) architecture that improves instruction throughput by exploiting instruction level parallelism (ILP) and thread level parallelism (TLP). The proposed architecture issues and completes instructions belonging to the same thread in exact program order. The issue and completion policy greatly reduces the design complexity and hardware cost of our architecture, compared with others that employ out-of-order issue and completion. On the other hand, when the instructions belong to different threads, the issue and completion orders for those instructions may not necessarily be identical to the fetch order. The processor issues instructions simultaneously from multiple threads to functional units by exploiting ILP and TLP, and by dynamic resource sharing. That parallel execution notably improves performance and resource utilization with minimal additional hardware cost over the conventional superscalar processors. This paper proposes an SMT architecture with grouping as well as one without grouping. Without grouping, all threads dynamically and flexibly share most resources. On the other hand, in the SMT architecture with grouping, in which resources and threads are divided into several groups for design simplification, resources are shared only among threads belonging to the same group as those resources. Simulation results show that our processors with four and eight threads improve performance by three or more times over the conventional superscalar processor with comparable execution resources and policies, and that reasonable grouping reduces the design complexity of SMT processors with little negative effect on performance.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10b
• /
• pp.1705-1711
• /
• 1991

On-line fault detection and diagnosis has an increasing interest in a chemical process industry, especially for a process control and automation. The chemical process needs an intelligent operation-aided workstation which can do such tasks as process monitoring, fault detection, fault diagnosis and action guidance in semiautomatic mode. These tasks can increase the performance of a process operation and give merits in economics, safety and reliability. Aiming these tasks, series of researches have been done in our lab. Main results from these researches are building appropriate knowledge representation models and a diagnosis mechanism for fault detection and diagnosis in a chemical process. The knowledge representation schemes developed in our previous research, the symptom tree model and the fault-consequence digraph, showed the effectiveness and the usefulness in a real-time application, of the process diagnosis, especially in large and complex plants. However in our previous approach, the diagnosis speed is its demerit in spite of its merits of high resolution, mainly due to using two knowledge models complementarily. In our current study, new knowledge representation scheme is developed which integrates the previous two knowledge models, the symptom tree and the fault-consequence digraph, into one. This new model is constructed using a material balance, energy balance, momentum balance and equipment constraints. Controller related constraints are included in this new model, which possesses merits of the two previous models. This new integrated model will be tested and verified by the real-time application in a BTX process or a crude unit process. The reliability and flexibility will be greatly enhanced compared to the previous model in spite of the low diagnosis speed. Nexpert Object for the expert system shell and SUN4 workstation for the hardware platform are used. TCP/IP for a communication protocol and interfacing to a dynamic simulator, SPEEDUP, for a dynamic data generation are being studied.