• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.27-30
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• 2022

The plasma process, which has many advantages in terms of efficiency and environment compared to conventional process methods, is widely used in semiconductor manufacturing. Plasma Sheath is a dark region observed between the plasma bulk and the chamber wall surrounding it or the electrode. The Plasma Sheath Monitoring Sensor (PSMS) measures the difference in voltage between the plasma and the electrode and the RF power applied to the electrode in real time. The PSMS data, therefore, are expected to have a high correlation with the state of plasma in the plasma chamber. In this study, a model for predicting the state of nitrogen ions in the plasma chamber is training by a deep learning machine learning techniques using PSMS data. For the data used in the study, PSMS data measured in an experiment with different power and pressure settings were used as training data, and the ratio, flux, and density of nitrogen ions measured in plasma bulk and Si substrate were used as labels. The results of this study are expected to be the basis of artificial intelligence technology for the optimization of plasma processes and real-time precise control in the future.

• KSBB Journal
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• v.19 no.1
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• pp.27-32
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• 2004

2-Dimensional fluorescence sensor has a wide range of excitation and emission wavelengths, that some biogenic fluorphors in a biological process can be monitored simultaneously. The production processes of 5-aminolevulinic aicd (ALA) by recombinant E. coli BL21 (DE3) pLysS harboring plasmid pFLS45 were on-line monitored by a 2-dimensional fluorescence sensor The characteristics of fluorescence spectrum was dependent upon physical and biological factors of a bioprocess such as culture pH, cell mass etc. Some off-line data were correlated to the fluorescence intensity well, which was monitored at some combination of excitation and emission wavelengths by the 2-dimensional fluorescence sensor.

• Nuclear Engineering and Technology
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• v.32 no.3
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• pp.261-273
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• 2000

During an extensive review made as part of the Integrated Diagnosis System project of the Maiden Reactor Project, MOAS (Maryland Operator Advisory System) was identified as one of the most thorough systems developed thus far. MOAS is an integrated on-line diagnosis system that encompasses diverse functional aspects that are required for an effective process disturbance management: (1) intelligent process monitoring and alarming, (2) on-line sensor data validation and sensor failure diagnosis, (3) on-line hardware (besides sensors) failure diagnosis, and (4) real-time corrective measure synthesis. The MOAS methodology was used at the Maiden Man-Machine Laboratory HAMMLAB of the OECD Maiden Reactor Project. The performance of MOAS, developed in G2 real-time expert system shell for the high-pressure preheaters of the NORS process in the HAMMLAB, was tested against a variety of transient scenarios, including failures of the control valves and sensors, and tube leakage of the preheaters. These tests showed that MOAS successfully carried out its intended functions, i.e., quickly recognizing an occurring disturbance, correctly diagnosing its cause, and presenting advice on its control to the operator. The lessons learned and insights gained during the implementation and performance tests also are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1854-1868
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• 2018

A wireless body-sensor network (WBSN) refers to a network-configured environment in which sensors are placed on both the inside and outside of the human body. The sensors are much smaller and the energy is more constrained when compared to traditional wireless sensor network (WSN) environments. The critical nature of the energy-constraint issue in WBSN environments has led to numerous studies on the reduction of energy consumption of WBSN sensors. The transmission-power-control (TPC) technique adjusts the transmission-power level (TPL) of sensors in the WBSN and reduces the energy consumption that occurs during communications. To elaborate, when transmission sensors and reception sensors are placed in various parts of the human body, the transmission sensors regularly send sensor data to the reception sensors. As the reception sensors receive data from the transmission sensors, real-time measurements of the received signal-strength indication (RSSI), which is the value that indicates the channel status, are taken to determine the TPL that suits the current-channel status. This TPL information is then sent back to the transmission sensors. The transmission sensors adjust their current TPL based on the TPL that they receive from the reception sensors. The initial TPC algorithm made linear or binary adjustments using only the information of the current-channel status. However, because various data in the WBSN environment can be utilized to create a more efficient TPC algorithm, many different types of TPC algorithms that combine human movements or fuse TPC with other algorithms have emerged. This paper defines and discusses the design and development process of an efficient TPC algorithm for WBSNs. We will describe the WBSN characteristics, model, and closed-loop mechanism, followed by an examination of recent TPC studies.

• Journal of IKEEE
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• v.18 no.1
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• pp.172-178
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• 2014

This paper describes the design technique and the method of analog front-end to measure the saturation of hemoglobin with oxygen sensor. To process the $SpO_2$ value from the sensor, the current data from the sensor should be converted into voltage domain. Designed analog front-end usually converts the current data from the sensor into voltage domain data to pass it on analog-to-digital converter called ADC with a different level of gain characteristics. This circuit was fabricated in a $0.11{\mu}m$ CMOS technology and has 4 level of gain properties. The occupied area is $0.174mm^2$.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.164-170
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• 2012

A Virtual Reality Modeling Language (VRML) database access in remote virtual reality control platform for dyeing enterprise $MiWi^{TM}$ thermal sensor network is presented in this paper. The VRML-ASP framework is introduced for 3D real-time data plotting in this application. The activities of thermal sensor nodes and sensor area are analyzed. The database access framework is optimized for $MiWi^{TM}$ wireless sensor networks. The experimental results show that VRML-ASP database access framework could be a reliable and competitive data-manage candidate for targeted virtual reality remote industrial visualization application.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1619-1624
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• 2008

Recently, In order to provide ubiquitous service, we develop each middleware and interface for sensors networks, RFID(Radio Frequency IDentification) and GPS(Global Positioning System). We use GPS for the location-based services, RFID for identifying the distribution logistics services, sensor networks for collecting the context data such as temperature, humidity, barometric pressure. Recently, there is increasing the need of integrated interface for using these sensor elements complexly in agriculture and livestock, construction, medical applications and, etc. This paper developments a integrated common interface for supporting efficiently process of sensing data using RFID, GPS and sensor network. This interface have the filtering function, parsing function, queuing function, interface management function, access function and, etc. We present the integrated frame structure combined with the collected context data in GPS, RFID and sensor networks and, support the open interface for providing complex ubiquitous application systems. Therefore, this interface will be efficiently used the system using RFID, GPS and sensor network, and supported many ubiquitous application services.

• Journal of Korean Society for Quality Management
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• v.52 no.1
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• pp.43-56
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• 2024

Purpose: The injection molding process, crucial for plastic shaping, encounters difficulties in sustaining product quality when replacing injection machines. Variations in machine types and outputs between different production lines or factories increase the risk of quality deterioration. In response, the study aims to develop a system that optimally adjusts conditions during the replacement of injection machines linked to molds. Methods: Utilizing a dataset of 12 injection process variables and 52 corresponding sensor variables, a predictive model is crafted using Decision Tree, Random Forest, and XGBoost. Model evaluation is conducted using an 80% training data and a 20% test data split. The dependent variable, classified into five characteristics based on temperature and pressure, guides the prediction model. Bayesian optimization, integrated into the selected model, determines optimal values for process variables during the replacement of injection machines. The iterative convergence of sensor prediction values to the optimum range is visually confirmed, aligning them with the target range. Experimental results validate the proposed approach. Results: Post-experiment analysis indicates the superiority of the XGBoost model across all five characteristics, achieving a combined high performance of 0.81 and a Mean Absolute Error (MAE) of 0.77. The study introduces a method for optimizing initial conditions in the injection process during machine replacement, utilizing Bayesian optimization. This streamlined approach reduces both time and costs, thereby enhancing process efficiency. Conclusion: This research contributes practical insights to the optimization literature, offering valuable guidance for industries seeking streamlined and cost-effective methods for machine replacement in injection molding.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.51-61
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• 2017

This paper proposes a method to generate panoramic images by combining conventional feature extraction algorithms (e.g., SIFT, SURF, MPEG-7 CDVS) with sensed data from an inertia sensor to enhance the stitching results. The challenge of image stitching increases when the images are taken from two different mobile phones with no posture calibration. Using inertia sensor data obtained by the mobile phone, images with different yaw angles, pitch angles, roll angles are preprocessed and adjusted before performing stitching process. Performance of stitching (e.g., feature extraction time, inlier point numbers, stitching accuracy) between conventional feature extraction algorithms is reported along with the stitching performance with/without using the inertia sensor data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.133-137
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• 2016

The video and audio information occupies a large portion of the IoT information. Various sensors can be used in a more accurate situation awareness and the absence of the main information has been required. Increasing in resource management in accordance with the use of various sensors. As a method to reduce the resources required in the communication of the various sensors and find the possibility to process the sensor information that can take the place of the other sensor. In this paper, using the LIS302 DL MEMS motion sensor to measure the data in the ping-pong ball, shuttlecock, tennis ball falling into table tennis. Data measured in the three object was confirmed that in proportion to the amount of impact. This experiment using the accelerometer can be confirmed that changes in the amount of impact. The results using a single multi-function sensor showed a possible implementation. In addition, the recognized in consideration of the situation in the early development stage of the multi-function sensor.