• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.75-81
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• 2024

In this paper, we proposes a method for real-time processing of inter-floor noise problems by embedding TinyML, which includes a deep learning model, into ultra-low-power systems. The reason this method is feasible is because of lightweight deep learning model technology, which allows even systems with small computing resources to perform inference autonomously. The conventional method proposed to solve inter-floor noise problems was to send data collected from sensors to a server for analysis and processing. However, this centralized processing method has issues with high costs, complexity, and difficulty in real-time processing. In this paper, we address these limitations by employing On-Sensor AI using TinyML. The method presented in this paper is simple to install, cost-effective, and capable of processing problems in real-time.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.9-16
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• 2017

In this paper, we propose a new routing protocol for wireless sensor networks. This protocol improves energy consumption of ML-LEACH by reducing the transmission distance of member node via Relay Node. Since clusters of each Layer in ML-LEACH are randomly formed, the distance, between member node and cluster head may be longer than specific distance, distance threshold value. To improve this, we propose the new routing protocol using 2-Hop transmission via Relay Node depending on the transmission distance of the member node.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.8-15
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• 2016

Studies have been made for the wireless sensor network protocols by a number of researchers to date. In particular, the studies as to the hierarchical protocol LEACH algorithm was concentrated. Various studies have been derived for the performance of the protocol is based on the LEACH protocol have been made. Improved algorithms have been proposed continuously. On the other hand, The performance comparison and evaluation of the improved algorithm is insufficient. Therefore, we compared the performance for the ML-LEACH (Multi Hop-Layered) and DL-LEACH (Dual Hop-Layered) been derived mainly LEACH. scalability, energy consumption, CH elected, network lifetime were selected as a Performance evaluation items.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.337-340
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• 2005

Context-awareness techniques in ubiquitous computing environment provide various services to users who need to get information via the analysis of collected information from sensors in a spatial area. Context-awareness has been increased in ubiquitous computing and is applied to many different applications such as disaster management system, intelligent robot system, transportation management system, shopping management system, and digital home service. Many researches have recently focused on services that provide the appropriate information, which are collected from Internet by different kinds of sensors, to users according to context of their surrounding environment. In this paper, we propose an abstraction model to manage the large-scale contextual information and their metadata which are collected from different kinds of in-situ sensors in a spatial area and are presented them on the web. This model is composed of the modules expressing functional elements of sensors using sensorML(Sensor Model Language) based on XML language and the modules managing contextual information, which is transmitted from the sensors.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1807-1810
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• 2002

This paper presents a technique for avoid- ing indefiniteness in Maximum Likelihood (ML) criteria for Direction-of-Arrival (DOA) finding using a sensor ar- ray with arbitrary configuration. The ML criterion has singular points in the solution space where the criterion becomes indefinite. Solutions fly iterative techniques for ML bearing estimation may oscillate because of numerical instability which occurs due to the indefiniteness, when bearings more than one approach to the identical value. The oscillation makes the condition for terminating iterations complex. This paper proposes a technique for avoiding the indefiniteness in ML criteria.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.443-451
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• 2023

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites.

• Korean Journal of Food Science and Technology
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• v.22 no.6
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• pp.686-689
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• 1990

For the monitoring of kimchi fermentation states, pressure detecting sensor and monitoring device were designed and fabricated. The system was consisted of an air tight fermenting tube(31.5 ml), strain gauge type pressure sensor and signal processing device built with operational amplifier and A/D converter, and interfaced to personal computer. Chiness cabbage kimchi was fermented in the plastic container($150{\times}220{\times}160mm$) at $25^{\circ}C\;and\;30^{\circ}C$. The fermentation was monitored with fermenting tubes containing kimchi. The pressure based kimchi fermentation curve was constructed and showed a typical kimchi curing curve having 2 stepwise pressure increasing pattern.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.362-369
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• 2012

Recently, a target detection based on a distributed sensor network has been much studied in active sonar. Zhou et al. proposed a target localization method using line fitting based on a distributed sensor network which consists of low complexity sensors that only report binary detection results. This method has three advantages relative to ML estimator. First, there is no need to estimate propagation model parameters. Second, the computation is simple. Third, it only use sensors with "detection", which implies less data to be collected by data processing center. However, this method has larger target localization error than the ML estimator. In this paper, a target localization method which modifies Zhou's method is proposed for reducing the localization error. The modified method shows the performance improvement that the target localization error is reduced by 40.7% to Zhou's method in the point of RMSE.

• Journal of the Korean Electrochemical Society
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• v.1 no.1
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• pp.40-44
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• 1998

The LAPS device of fast response and high sensitivity, based on electrochemical potential difference, and its system were fabricated for the precise measurement of pH changes and its characteristic were investigated. The electrostatic variation characteristics of LAPS according to the pH changes and parameters in the device were verified through a simulation using LAPS equivalent circuit model. The LAPS device and its system were fabricated on the basis of the result of simulation. The fabricated LAPS system showed linear sensitivity (about 56 mV/pH within the range of pH 2 to pH 11. In order to overcome the defect of general urea sensor (especially slow response time), urease immobilized nitrocellulose membrane was attached on the LAPS and resulted in the very fast response time, 0.29 mV/sec, 0.86 mV/sec at urea concentration of $50{\mu}g/ml,\; 500{\mu}g/ml$, respectively. And also in order to measure the uranyl ion, the uranyl ion selective sensing membrane with calix[6]arene derivative was used and its sensitivity was 25mV/concentration decade in the wide uranyl ion concentration range of $10^{-11}M\;to\;10^{-4}M$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.628-634
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• 2016

This paper demonstrates a microfluidic electrochemical sensor for detecting endocrine disruptor such as estradiol at a very low concentration by using preconcentration technique. In addition, self-assembled monolayer(SAM) was also employed on the working electrode of the electrochemical sensor in order to increase the estradiol capture efficiency of the sensor. SAM treatment on the working electrode enhanced the specific binding between the surface of the working electrode and the estradiol antibody. The estradiol antibody was applied on the working electrode at different concentrations(10, 20, 50, 100, 200 pg/ml) for observing the concentration dependency. The measured electrochemical redox current changed with the amount of the bound estradiol on the Au working electrode surface and the sensor can detect all the target material when the immobilized antibody amount is more than the estradiol amount in the water. The elecrochemical estradiol sensor without SAM treatment showed a low current of 7.79 nA, while the sensor treated with SAM resulted in 339 nA at 200 pg/ml, which is more than 40 fold higher output current. When combining the preconcentration technique and the SAM-treated electrode, the measured current became more than 100 fold higher than that of the sensor without neither SAM treatment nor preconcentration technique. The combination of these two techniques can would enable the proposed microfluidic electrochemical sensor to detect a very low concentration endocrine disruptor.