• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2480-2487
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• 2011

As sensor technology grows up in fields such as environmental hazards detecting system, ubiquitous sensor network, intelligent robot, the sensing and detecting system for sensor is increasing. The sensor data is measured by change of chemical and physical status. Because of decrepit sensor or various sensing environment, it is problem that sensor data is inaccurate result. So the reliability of sensor data is essential. In this paper, we proposes a reliable sensor signal processing platform for various sensor. To improve reliability, we use same sensors in multiple array structure. As sensor data is corrected by spatial and temporal relation signal processing algorithm for measured sensor data, reliability of sensor data can be improved. The exclusive protocol between platform components is designed in order to verify sensor data and sensor state in various environment.

• Journal of Astronomy and Space Sciences
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• v.18 no.2
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• pp.163-173
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• 2001

In this paper, space radiation environment and single event effect(SEE) have been analyzed for the KOMPSAT-2 operational orbit. As spacecraft external and internal space environment, trapped proton, SEP(solar energetic particle) and GCR(galactic cosmic ray) high energy Protons and heavy ions spectrums are analyzed. Finally, SEU and SEL rate prediction has been performed for the Intel 80386 microprocessor CPU that is planned to be used in the KOMPSAT-2. As the estimation results, under nominal operational condition, it is predicted that trapped proton and high energetic proton induced SBU effect will not occur. But, it is predicted that heavy ion induced SEU can occur several times during KOMPSAT-2 3-year mission operation. KOMPSAT-2 has been implementing system level design to mitigate SEU occurrence using processor CPU error detection function of the on-board flight software.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.8
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• pp.813-817
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• 2008

Intelligent Robots (IR) get data of the current situation from sensors and perform knowledgeable services. Self-reconfiguration of IR is an important factor to change itself without stopping while supporting environment and technology change. In this paper, we propose an agent based self-reconfiguration framework of IR using ESB (Enterprise Service Bus). This framework focuses on dynamic discovery and reconfiguration of service-oriented applications using multi-agent system in intelligent robots. When IR meets an irresolvable situation it downloads a necessary service agent from an external service repository, executes the agent, and resolves the situation. Agent technology provides an intelligent approach for collaborations of IR. The prototype has also been implemented to show the validity of our study.

• Convergence Security Journal
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• v.12 no.6
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• pp.85-92
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• 2012

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical Process Control (SPC) can monitor the forecasting of software failure and there by contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of log Poission, log-linear and Parto distribution.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1779-1785
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• 2018

In this paper, we propose an error correction method to improve the accuracy of human activity recognition using sensor event data obtained by smart devices such as wearable and smartphone. In the context awareness through the smart device, errors inevitably occur in sensing the necessary context information due to the characteristics of the device, which degrades the prediction performance. In order to solve this problem, we apply Kalman filter's error correction algorithm to compensate the signal values obtained from 3-axis acceleration sensor of smart device. As a result, it was possible to effectively eliminate the error generated in the process of the data which is detected and reported by the 3-axis acceleration sensor constituting the time series data through the Kalman filter. It is expected that this research will improve the performance of the real-time context-aware system to be developed in the future.

• The Journal of the Acoustical Society of Korea
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• v.19 no.5
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• pp.13-19
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• 2000

The blind segmentation method, which segments input speech data into recognition unit without any prior knowledge, plays an important role in continuous speech recognition system and corpus generation. As no prior knowledge is required, this method is rather simple to implement, but in general, it suffers from bad performance when compared to the knowledge-based segmentation method. In this paper, we introduce a method to improve the performance of a blind segmentation of Korean continuous speech by postprocessing the segment boundaries obtained from the blind segmentation. In the preprocessing stage, the candidate boundaries are extracted by a clustering technique based on the GLR(generalized likelihood ratio) distance measure. In the postprocessing stage, the final phoneme boundaries are selected from the candidates by utilizing a simple a priori knowledge on the syllabic structure of Korean, i.e., the maximum number of phonemes between any consecutive nuclei is limited. The experimental result was rather promising : the proposed method yields 25% reduction of insertion error rate compared that of the blind segmentation alone.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.6
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• pp.483-490
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• 2015

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do likelihood inference for software reliability models based on finite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The infinite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outlier, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, proposed a control mechanism based on NHPP using mean value function of Musa-Okumo and Power law type property.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.5
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• pp.38-48
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• 2007

In ubiquitous environment, sensor networks that sense and transmit surrounding data without human intervention will become more important. If sensors are installed for detecting vehicles and measuring their speed in the road and that real-time information is given to drivers, it will be very effective for enhancing safety and controlling traffic in the road. In this paper, we proposed a new reliable and real-time sensor MAC protocol between AP and sensor nodes in order to provide real-time traffic flow information based on ubiquitous sensor networks. The proposed MAC allocates one TDMA slot for each sensor node on the IEEE 802.15.4 based channel structure, introduces relayed communication for distant sensors, and adopts a frame structure that supports retransmission for the case of errors. In addition, the proposed MAC synchronizes with AP by using beacon and adopts a hybrid tracking mode that supports economic power consumption according to various traffic situations, We implemented a simulator for the proposed MAC by using sim++ and evaluated various performances. The simulation results show that the proposed MAC reduces the power consumption and reveals excellent performance in real-time application systems.

• Journal of Appropriate Technology
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• v.5 no.2
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• pp.70-81
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• 2019

The garment industry is one of the most labor-intensive manufacturing industries, with its sewing process relying almost entirely on manual labor. Its costs highly depend on the efficiency of this production line and thus is crucial to determine the production rate in real-time for line balancing. However, current production tracking methods are costly and make it difficult for many Small and Medium-sized Enterprises (SMEs) to implement them. As a result, their reliance on manual counting of finished products is both time consuming and prone to error, leading to high manufacturing costs and inefficiencies. In this paper, a production tracking system that uses the sewing machines' energy consumption data to track and count the total number of sewing tasks completed through Convolutional Neural Network (CNN) classifiers is proposed. This system was tested on two target sewing tasks, with a resulting maximum classification accuracy of 98.6%; all sewing tasks were detected. In the developing countries, the garment sewing industry is a very important industry, but the use of a lot of capital is very limited, such as applying expensive high technology to solve the above problem. Applied with the appropriate technology, this system is expected to be of great help to the garment industry in developing countries.