• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.915-927
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• 2008

Networked control systems(NCSs) have gained increasing attention in recent years due to their advantages and potential applications. The network Quality-of-Service(QoS) in NCSs always fluctuates due to changes of the traffic load and available network resources. To handle the network QoS variations problem, this paper presents an intelligent scheduling control method for NCSs, where the sampling period and the control parameters are simultaneously scheduled to compensate the effect of QoS variation on NCSs performance. For NCSs with network-induced delays and packet dropouts, a discrete-time switch model is proposed. By defining a sampling-period-dependent Lyapunov function and a common quadratic Lyapunov function, the stability conditions are derived for NCSs in terms of linear matrix inequalities(LMIs). Based on the obtained stability conditions, the corresponding controller design problem is solved and the performance optimization problem is also investigated. Simulation results are given to demonstrate the effectiveness of the proposed approaches.

• Journal of the Korean Geotechnical Society
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• v.37 no.9
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• pp.25-36
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• 2021

Recently, the intelligent compaction technology for quality control of earthworks has brought attention as a quality control standard for earthworks. In this study, intelligent compaction technology and earthwork quality control methods were investigated and earthwork quality control procedures using intelligent compaction technology were considered based on field tests. Through the field compaction test of the silty sand (SM) fill material, it was confirmed that CMV and bearing capcaity index from plate load tests increased as the number of compactions increased. Based on the field test data, the average CMV and quality control target CMV were derived. The target CMV (34.2) was calculated through the correlation with the bearing capacity index of the plate load test, and the target CMV (36.6) was calculated through the analysis of the CMV increase rate. In this paper, the on-site compaction quality management procedure and methodology using intelligent compaction technology were discussed, and an intelligent compaction quality management method was proposed to promote the applicability of the technology.

• Advances in concrete construction
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• v.14 no.1
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• pp.57-70
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• 2022

The spiral case concrete (SCC) used in the underground powerhouse of large hydropower stations is complex, difficult to pour, and has high requirements for temperature control and crack prevention. In this study, based on the closed-loop control theory of "multi-source sensing, real analysis, and intelligent control", a new intelligent cooling control system (ICCS) suitable for the SCC is developed and is further applied to the Wudongde large-scale underground powerhouse. By employing the site monitoring data, numerical simulation, and field investigation, the temperature control quality of the SCC is evaluated. The results show that the target temperature control curve can be accurately tracked, and the temperature control indicators such as the maximum temperature can meet the design requirements by adopting the ICCS. Moreover, the numerical results and site investigation indicate that a safety factor of the spiral case structure was sure, and no cracking was found in the concrete blocks, by which the effectiveness of the system for improving the quality of temperature control of the SCC is verified. Finally, an intelligent cooling control procedure suitable for the SCC is proposed, which can provide a reference for improving the design and construction level for similar projects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.365-380
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• 2022

The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.

• Journal of the Korean Geotechnical Society
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• v.39 no.11
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• pp.85-95
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• 2023

This study implemented intelligent compaction technology at the construction site of the AY Highway in Gyeonggi Province, with a focus on obtaining the representative intelligent compaction value, CMV. The target CMV for quality control was established through trial construction, and the validation of the compaction quality control process based on intelligent compaction was conducted. The optimal approach for determining the target CMV was confirmed to be through linear regression of the average CMV measured within a 5-m radius from the plate load testing location. Upon assessing compaction quality against the target CMV, it was observed that the quality criteria outlined in the domestic intelligent compaction standard were met. However, the criteria outlined in Austria and the United States were not satisfied. Notably, indicators related to the variability of compaction quality did not meet the specified criteria, suggesting a stringent standard compared to the observed variability of CMV, ranging from 17% to 55%. Consequently, it is recommended to conduct additional field tests to further validate the compaction quality control process based on intelligent compaction. This will aid in confirming and enhancing the appropriateness of the regulations stipulated in each standard.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.2
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• pp.292-297
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• 2013

Nowadays, a strategy of the self-optimizing machining process is an imperative approach to improve the product quality and increase productivity of manufacturing systems. This paper presents a concept of self-optimizing forming system that allows the forming system automatically to adjust the forming parameters online for guarantee the product quality and avoiding the machine stop. An intelligent monitoring system that has the functions of observation, evaluation and diagnostic is developed to evaluate the pully quality during forming process. Any abnormal variation of forming machining parameters could be detected and adjusted by an intelligent control system aiming to maintain the machining stability and the desired product quality. This approach is being practiced on the pully forming machine for evaluating the efficiency of the proposed strategy.

• International Journal of Railway
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• v.7 no.3
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• pp.80-84
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• 2014

The purpose of this paper was to review Intelligent Compaction (IC) techniques, which is regarded relatively new to the railroad roadbed construction activity. Most of civil structures are built on roadbed that supposed to provide adequate load bearing support to the upper structure through the qualified compaction process. However, it is not uncommon for structure failure attributed to inadequate compaction control take place in field sites. Unlike traditional compaction control method to check field density at several locations, IC techniques continuously measure various compaction quality indices that represent compaction uniformity. In this paper, a series of literature review relevant to IC techniques was conducted to provide concise summary on the following categories: 1) background of IC technique; 2) Summary of IC vendors and basic principles; 3) modeling of IC behavior, and 4) case study along with correlation between IC with other measurements. In summary, IC technologies seem to be promising in future railroad construction to achieve better compaction quality control so that the serviceability of railroad can be ensured with minimizing rehabilitation and maintenance activities.

• Journal of Korean Library and Information Science Society
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• v.21
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• pp.401-429
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• 1994

According to database and information technology development, many interests of database quality control have being increase. The purpose of database quality control is improvement quality of data itself as well as database system to satisfy user's need. As this paper was especially written about quality control of bibliographic database, to embody complete bibliographic database, it was invested numerous errors and its case by analyzing MARC records. In addition, it was presented that high degree's cataloging education, introduction of su n.0, pporting systems, and development of intelligent quality control system for quality improvement.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1159-1162
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• 1993

We describe six important defuzzification methods and their respective merits and shortcomings, dependent on the rules, domains, etc. Furthermore, we present an alternative approach, the so called ξ-Quality defuzzification method, for the case that the output fuzzy sets have different shape or are asymmetric.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.652-658
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• 1997

Coagulant feeding control is very important in the water treatment process. Coagulant feeding is related to the raw water quality such as turbidity, alkalinity, water temperature, pH and so on. However, since the process of chemical reaction has not been clarified so far, coagulant dosing rate has been decided by jar-test. In order to overcome the difficulty mentioned above, Fuzzy Neural Network to fuse fuzzy logic and neural network was proposed, and the scheme was applied to the automatic determination of coagulant dosing rate. This algorithm can automatically identify the if-then rules, tune the membership functions by utilizing expert's experimental data. The proposed scheme is evaluated by computer simulation and interfaced with coagulant feeder operated by magnetic flowmeter, control valve and PLC. It is shown that coagulant feeding according to real time sensing of water quality is very effective.