• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.12
• /
• pp.1113-1119
• /
• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• The Journal of Information Systems
• /
• v.6 no.1
• /
• pp.223-256
• /
• 1997

Since the business scales of retailing companies become to be very large and the number of items dealt increases explosively, automation of inventory management becomes one of the most important issues to solve in retailing industry. In order to accomplish this automation of inventory management, there must be a great need to a method which can perform real-time decision making on inventory control in an automatic fashion, while communicating with inventory information systems like POS system and automatic warehousing system. But even in this circumstance, there are also many obstructions to such automation like varying demands, limited capacity of warehouse and exhibition room, need for strategic consideration on inventory control, etc., in a real sense. Due to these reasons, it seems very difficult that most large-scaled retailing companies get fully automated inventory management system. To overcome those difficulties and reflect them into inventory control, we propose a automated inventory control methodology for retailing industry based on neural network and policy model. Especially, policy model is devised to deal with dynamic varying demands and using this model, strategic goals on inventory can be considered into inventory control mechanism. Our proposed approach is implemented in workstation and its performance is also empirically verified also against to real case of one of the major retailing firm in Korea.

• International Journal of Aeronautical and Space Sciences
• /
• v.11 no.4
• /
• pp.326-337
• /
• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Journal of Korean Society for Atmospheric Environment
• /
• v.13 no.2
• /
• pp.137-145
• /
• 1997

To predict the effects of air pollutant in the coastal region, we have developed the air pollutant model, the reaction model and the deposition of NO, $NO_2, and O_3$. And the numerical model of air pollutant concentration employed the nested technique to calculate with the higher resolution for the area. The nested technique used two grid systems, one for the large scale calculating region with the coarse mesh grid (CMG) and the other for the small scale region with the fine grid (FMG). In other to prove the validity of the simulation model the calculations were conducted for the present situation. The results of them reasonably agree with the observed data and proved the validity of the model.

• Journal of the Korea Institute of Building Construction
• /
• v.15 no.3
• /
• pp.339-349
• /
• 2015

A large-scale research project to develop a robot-based automated building construction system for steel structures was successfully conducted in South Korea. This paper discusses the results of the real-scale test and the key lessons gained from the testing process. The system was assessed in terms of system productivity, construction cost, quality control, and safety improvements. While the productivity of the automated system showed an improvement of about 9.5%, the construction cost was about six times higher than that of the conventional method. The field test also indicated that the automated system requires more on-site quality control measures. However, because the system can eliminate the causes of various safety accidents, safety levels might be expected to be improved significantly. It is expected that this paper will provide knowledge and insight for developing new systems, and the results of the real-scale test might be useful for other researchers and similar research projects in the future.

• Journal of Technologic Dentistry
• /
• v.18 no.1
• /
• pp.73-94
• /
• 1996

This study was aimed for evaluating the validity of relative-productivity index on the basis of working hour(RPHW index) designed by author as new productivity index and drawing up a plan of bettering productivity to cope with financial difficulty of dental laboratories. Fortyeight dental laboratories extracted by cluster-sampling method form all the dental laboratories in Seoul were subjected for this study. And in each of them, the term of establishment which was divied into three group as short-term, mid-term and long-term group, the scale of dental laboratory which as divided into two group by number of dental technician as small-scale group and large-scale group, the salary system which was composed of salary criteria, pay according to ablility criteria and collectiveagree, the salary level, simple labor productivity index(SLP index), relative-productivity index on the basis of worker number(ROWN) and relative-productivity index on the basis of working hour(RPWH index) were surveyed through the self-administractive questionaires. The results as follows : Most of dental laboratories(93.6%) were managed by non-professional managers. The establishment rate per year of dental laboratory was the increase. The mean number of employees of dental laboratories was $7.00{\pm}3.90$ person. The ratio of smallscale group(under the 5 employees) was 42.6% and as the term of establishment was shorter, the ratio of small-scale group was higher. The mean establishment area of dental laboratories was $24.49{\pm}10.97$ unit and the mean establishment area per head of dental laboratories was $4.05{\pm}3.90$ unit. The estabilshment area and area per head were not significantly associted with the term of establishment, but as the term of establishment was shorter, the estabkishment area per head was slightly wider. The establishment area per head in small-scale group was significantly wider than large-scale group(over the 6 emplayees) The salary criteria(54.4%), pay according to ability(79.2)m ability criteria(77.1%) and collectiveagree(79.2%) as salary systems were used in the most of all dnetal laboratoies. The all salary systems were not significantly associated with the term of establishment and the scale of dental laboratories. The monthly mean salary level of dental laboratoies was $125.64{\pm}31.06$ milion won. The monthly salary level was not significantly associated with the term of establishment and the scale of dental laboratores. But the monthly salary level in the short-term group and the small-scale group were slightly lower than others. The SLP index, the RPWN index and the RPWH index of dental laboratories were $132.16{\pm}48.41$, $382.41{\pm}128.76$ and $76.06{\pm}25.11$, respectively. The SLP, the RPWN and the RPWH of dental laboratories were not significantly associated with the term SLP, the RPWN and the RPWH of dental laboratories were significantly associated with the area of dental laboratory and the salary level. Except for only the association salary criteria among salary system with RPWH of dental laboratories, all other salary system were not associated with the SLP, the RPWN and the RPWH of dental laboratories.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.1376-1378
• /
• 2004

When an underground pipeline runs parallel with DC traction systems, it suffers from DC traction interference. Because the train is fed by the substation through the overhead wire and return current back to the substation via the rails. If these return rails are poorly insulated from earth, DC current leak into the earth and can be picked up by nearby pipeline. It may bring about large-scale accidents even in cathodically protected systems. In this paper we analyze the cathodic protection systems of buried pipelines and DC traction stray current influence on it using the simulation software CatPro. We can discuss the problems and mitigation of DC traction interference for protected pipeline.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2002.05a
• /
• pp.1079-1084
• /
• 2002

Systems engineering technology development program for Korea next-generation high-speed railway(KNHR) system in progress is a national large-scale system development program that is not only a large-size and complex but also multi-disciplinary in nature. Using the RDD-IOO, a systems engineering tool, the KNHR program can establish requirements traceability and development process management in the course of development. This paper presents the results from a computer-aided systems engineering application to KNHR system technology development project over the three years of activities. The traceability among the system design database in the vertical direction of SE process, as the results of the first year and the second year research was accomplished. The database in both the requirement management domain and the project management domain was developed and set up the traceability between them in the horizontal direction of the SE process in the V model as the results of the third year research. Therefore, KNHR design database was built to support the life-cycle management of the system as well as to reuse the knowledge in future programs. In the following development phase, this database will be utilized to accomplish the test and integration activities providing a baseline database. The outcome of the study contributes to the establishment of the model-based systems engineering approach as a best practice in the accumulation and advancement of systems engineering technology for railway system development.

• Smart Structures and Systems
• /
• v.31 no.1
• /
• pp.69-78
• /
• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.1
• /
• pp.60-69
• /
• 2024

The importance of geospatial information is increasingly highlighted in the defense domain. Accurate and up-to-date geospatial data is essential for situational awareness, target analysis, and mission planning in millitary operations. The use of high-resolution geospatial data in military operations requires large storage and fast image processing capabilities. Efficient image processing is required for tasks such as extracting useful information from satellite images and creating 3D terrain for mission planning, In this paper, we designed a cloud-based operational situation mixed reality visualization system that utilizes large-scale geospatial information distributed processed on a cloud server based on the container orchestration platform Kubernetes. We implemented a prototype and confirmed the suitability of the design.