• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.92-100
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• 2012

A map of complex environment can be generated using a robot carrying sensors. However, representation of environments directly using the integration of sensor data tells only spatial existence. In order to execute high-level applications, robots need semantic knowledge of the environments. This research investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The proposed system is decomposed into five steps: sequential LIDAR scan, point classification, ground detection and elimination, segmentation, and object classification. This method could classify the various objects in urban environment, such as cars, trees, buildings, posts, etc. The simple methods minimizing time-consuming process are developed to guarantee real-time performance and to perform data classification on-the-fly as data is being acquired. To evaluate performance of the proposed methods, computation time and recognition rate are analyzed. Experimental results demonstrate that the proposed algorithm has efficiency in fast understanding the semantic knowledge of a dynamic urban environment.

• Korean Journal of Artificial Intelligence
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• v.6 no.1
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• pp.1-10
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• 2018

Compressed Air is an important energy source used in most factories nowadays. The automation trend using air compressor has been gradually increasing with the interest of the 4th industry in recent years. With the air compressor system, it is possible to construct the device at low cost and easily achieve automation and energy saving. In addition, With trend of FA, miniaturation and light weight manufacturing trend expand their use in the electronics, medical, and food sectors. Research method is to design the technology for the remote control of the following information as USN base. Development of flexible sensing module from real time observation module for fusion of IT technology in compressed air systems, design and manufacture of flexible sensing module, and realiability assessment. Design of real-time integrated management system for observation data of compressed air system - Ability to process observation data measured in real time into pre-processing and analysis data. This study expects unconventionally decreasing effect of energy cost that takes up 60~70% of air compressor layout and operation and maintenance management cost through USN(Ubiquitous Sensor Network) technology by using optimum operational condition from real time observation module. In addition, by preventing maintenance cost from malfunction of air compressor beforehand, maintenance cost is anticipated to cut back.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.369-377
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• 2011

Since well-calibrated satellite data is critical for their applications, calibration and validation of COMS science data was one of the key activities during the IOT. COMS MI radiometric calibration process was divided into two phases according to the out-gassing of the sensor: calibrations of the visible (VI) and infrared (IR) channels. Different from the VIS calibration, the calibration steps for the IR channels followed additional processes to secure their radiometric performances. Primary calibration steps of the IR were scan mirror emissivity correction, midnight effect compensation, slope averaging and 1/f noise compensation after a nominal calibration. First, the scan mirror emissivity correction was conducted to compensate the variability of the scan mirror emissivity driven by the coating material on the scan mirror. Second, the midnight effect correction was performed to remove unreasonable high spikes of the slope values caused by the excessive radiative sources during the local midnight. After these steps, the residual (difference between the previous slope and the given slope) was filtered by a smoothing routine to eliminate the remnant random noises. The 1/f noise compensation was also carried out to filter out the lower frequency noises caused from the electronics in the Imager. With through calibration processes during the entire IOT period, the calibrated IR data showed excellent performances.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.1
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• pp.103-109
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• 2006

In this paper, VDS(Vehicle Dynamics Simulator) and ACS(Attitude Control Simulator) are developed and are verified using PILS(Process In-the Loop Simulation) between VDS and ACS. VDS is including the AOCS(Attitude & Orbit Control Subsystem) hardware modeling of geosynchronous satellite and consists of modulation concept. ACS performs the attitude determination using sensor data and generates the attitude control commands. In order to transfer the data between VDS and PCDU(Power Control & Distribution Unit), data acquisition boards were mounted. VDS performance is verified using HILS(Hardware In-the Loop Simulation) between VDS and PCDU.

• Journal of Sensor Science and Technology
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• v.13 no.6
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• pp.436-445
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• 2004

In this study, a uroflowmetry system was developed to detect a voiding symptom conveniently at home or hospital. A implemented hardware was composed of mechanism and system circuit part, the software was developed to process uroflow data, graph display, extraction of parameter, and evaluation of congregate rate so as to analysis obtaining uroflow data. The following experiment was performed to evaluate an ability of classification and fitness. The curve pattern of uroflow was classified into each symptom. Various parameters were calculated in the curve pattern of each uroflow as follows. The parameters are MFR, AFR, VOL, VT, and FT. A significant difference among parameters was examined by a statistical analysis for extracted parameters between normal and abnormal experimental group. The uroflow data with the various symptom was divided into normal and abnormal group using fuzzy classifier. The result of the fuzzy classification using MFR and AFR was superior by 91.23 % than grouping evaluation including VOL.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.496-500
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• 1998

Not merely running at the designated constant speed as the classical cruise control, the adaptive cruise control (ACC) maintains safe headway distance when the front is blocked by other vehicles. One of the most essential part of ACC System is the range sensor which can measure the position and speed of all objects in front continuously, ignore all irrelevant objects, distinguish vehicles in different lanes and lock on to the closest vehicle in the same lane. In this paper, the hierarchical object recognition algorithm (HORA) is proposed to process raw scanning laser data and acquire valid distance to target vehicle. HORA contains two principal concepts. First, the concept of life quantifies the reliability of range data to filter off the spurious detection and preserve the missing target position. Second, the concept of conformation checks the mobility of each obstacle and tracks the position shift. To estimate and predict the vehicle position Kalman filter is used. Repeatedly updated covariance matrix determines the bound of valid data. The algorithm is emulated on computer and tested on-line with our ACC vehicle.

• Journal of the Korean Society of Safety
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• v.18 no.3
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• pp.154-160
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• 2003

Uncertain time delay happens when the process reads the sensor data and sends the control input to the plant located at a remote site in distributed control system. As in the case of data network using TCP/IP, VDN that integrates both device network and data network has uncertain time delay. Uncertain time delay can cause degradation in performance and stability of distributed control system based on VDN. This paper first investigates the transmission characteristic of VDN and suggests a control scheme based on the Smith's predictor to minimize the effect of uncertain varying time delay. The validity of the proposed control scheme is demonstrated with real-time velocity control of DC servo motor located in remote site.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.18-30
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• 2022

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.646-648
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• 2021

Sensors with wireless communication functions are essential for acquiring and transmitting spatio-temporal data that is not easily accessible in sensor network environments. However, these sensors lack adaptability to large amounts of sensing data processing or dynamic environments, resulting in over-consumption of power and network overhead. This paper proposes a mobile agent that can acquire, transmit, and process only the necessary data by applying thresholds, and presents methods for autonomous migration and communication processing of mobile agents.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1242-1251
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• 2009

A DSMS(Data Stream Management System) in ubiquitous environment processes huge data that input from a number of sensor. The existed system is used with a load shedding method that is eliminated with a part of huge data stream when it doesn't process the huge data stream. The Load shedding method has to filter a part of input data. This is because, data completeness or reliability is decreased. In this paper, we proposed the overload prediction load balancing to maintain data completeness when the system has an overload. The proposed method predicts the overload time. and than it is decreased with data loss when achieves the prediction overload time. The performance evaluation shows that the proposed method performs better than the existed method.