• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1233-1245
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• 2004

Though biometrics to authenticate a person is a good tool in terms of security and convenience, typical authentication algorithms using biometrics may not be executed on resource-constrained devices such as smart cards. Thus, to execute biometric processing on resource-constrained devices, it is desirable to develop lightweight authentication algorithm that requires only small amount of memory and computation. Also, among biological features, face is one of the most acceptable biometrics, because humans use it in their visual interactions and acquiring face images is non-intrusive. We present a new face authentication algorithm in this paper. Our achievement is two-fold. One is to present a face authentication algorithm with low memory requirement, which uses support vector machines (SVM) with the feature set extracted by genetic algorithms (GA). The other contribution is to suggest a method to reduce further, if needed, the amount of memory required in the authentication at the expense of verification rate by changing a controllable system parameter for a feature set size. Given a pre-defined amount of memory, this capability is quite effective to mount our algorithm on memory-constrained devices. The experimental results on various databases show that our face authentication algorithm with SVM whose input vectors consist of discriminating features extracted by GA has much better performance than the algorithm without feature selection process by GA has, in terms of accuracy and memory requirement. Experiment also shows that the number of the feature ttl be selected is controllable by a system parameter.

• Journal of the Korean Magnetics Society
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• v.24 no.1
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• pp.22-27
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• 2014

North indicating azimuth angle sensors have been used in airplanes, ships traditionally and nowadays employed in smart phones. For the azimuth and roll angle measurement of the sensor, 3-axis acceleration sensor was added to the 3-axis magnetic field sensor. In this work, we have constructed a measuring system for the measurement of the magnetic field and the angle uncertainty of the magnetic field sensors. Measuring system could be useful not only in non-magnetic laboratory but also in normal laboratory, we constructed small size of 3-axis Helmholtz coils for the compensation environment magnetic field (Earth magnetic field and magnetic field from building) and the generation of magnetic field for the test of magnetic field sensor. The constructed measuring system could compensate environment magnetic field below 10 nT level and generate 3-dimensional magnetic field with magnitude uncertainty of 0.2 % and angle error of $0.2^{\circ}$ within the volume of ${\pm}30mm$ diameter at center of Helmholtz coils. For the conformation of developed measuring system, We tested commercially available 3-axis magnetometer and heading sensor.

• Journal of the Korean Electrochemical Society
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• v.26 no.4
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• pp.43-55
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• 2023

Mass transport through nanoporous structures such as nanopores or nanochannels has fundamental electrochemical implications and many potential applications as well. These structures can be particularly useful for water treatment, energy conversion, biosensing, and controlled delivery of substances. Earlier research focused on creating nanopores with diameters ranging from tens to hundreds of nanometers that can selectively transport cationic or anionic charged species. However, recent studies have shown that nanopores with diameters of a few nanometers or even less can achieve more complex and versatile transport control. For example, nanopores that mimic biological channels can be functionalized with specific receptors to detect viruses, small molecules, and even ions, or can be made hydrophobic and responsive to external stimuli, such as light and electric field, to act as efficient valves. This review summarizes the latest developments in nanopore-based systems that can control mass transport based on the size of the nanopores (e.g., length, diameter, and shape) and the physical/chemical properties of their inner surfaces. It also provides some examples of practical applications of these systems.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.239-245
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• 2016

In this study, a 12 V PWM boost converter was designed with the optimal values of the external components of the power stage was well as the compensation stage for smart electronic applications powered by a battery device. The 12 V boost PWM converter consisted of several passive elements, such as a resistor, inductor and capacitor with a diode, power MOS switch and control IC chip for the control PWM signal. The devices of the power stage and compensation stage were designed to maintain stable operation under a range of load conditions as well as achieving the highest power efficiency. The results of this study were first verified by a simulation in SPICE from calculations of the values of major external elements comprising the converter. The design was also implemented on the prototype PCBboard using commercial IC LM3481 from Texas Instruments, which has a nominal output voltage of 12 V. The output voltage, ripple voltage, and load regulation with the line regulation were measured using a digital oscilloscope, DMM tester, and DC power supply. By configuring the converter under the same conditions as in the circuit simulation, the experimental results matched the simulation results.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.467-474
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• 2010

Recently, Study of functional clothing for Vital sensing is focused on reducing artifact by human motions, in order to enhance the electrocardiogram(ECG) sensing accuracy. In this study, considering the factors for each element found from the analysis, a 3-lead electrode inside textile embroidered with silver yarn was developed, and draft designs off our types of vital-signal sensing garments, which are 'chest-belt typed' garment, 'cross-typed' garment 'x-typed' garment and 'curved x-typed' garment, were prepared. The draft designs were implemented on a sleeveless male shirt made of an elastic material so that the garment and the electrodes can remain closely attached along the contour of the human body, and the acquired data was sent to the main computer over a wireless network. In order to evaluate the effects caused by body movements and the ECG-sensing capability for each type in static and dynamic states, displacements were measured from one and two dimensional perspectives. ECG measurement evaluation was also performed for Signal-to-noise ratio(SNR) analysis. Applying the experimental results, the draft garment designs were modified and complemented to produce two types of modular approaches 'continuous-attached' and 'insertion-detached' for the ECG-sensing smart clothing.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.118-131
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• 2012

The existing electronic tagging system traces the location of a sex offender through communicating with GPS satellites and mobile phone base stations in order to prevent repeated crimes. However, the GPS satellite communication method does not work well in the interiors of downtown buildings or on the subways where it is difficult to receive satellite signals. In such cases, the location can be traced through communication with mobile phone base stations. But the distance between mobile phone base stations is several hundred meters, and as a result the margin of error for location tracing can be maximum of 2km in accuracy reduction. Take for example, if a kindergarten is located on the 2nd floor and a coffee shop and the sex offender are located on the 3rd floor in a 5-story building that is downtown, the existing electronic tagging system cannot trace the location of the sex offender as the GPS satellite communication does not work in the interior of the building and the exact floor that the sex offender is located on cannot be recognized through communication with mobile phone base stations. This occurrence is a big problem for the existing electronic tagging system, which is based on position recognition. Therefore, this study suggests a smart electronic tagging system that can monitor sex offenders by using a Ubiquitous Sensor Network in such extreme situations where position recognition is not possible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.60-68
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• 2019

For effective building maintenance, the collection and management of various maintenance date such as work history information and material information is required. Despite the introduction of information management technologies and systems to reduce the amount of maintenance work of buildings, which have become larger and more complex, the maintenance information is not being utilized properly. As an alternative, research on the introduction and utilization of BIM is being conducted continuously. However, the BIM models generated at the design phase are not utilized in practice due to a lack of architectural maintenance information. This study proposed a COBie document prototype to support BIM-based smart maintenance tasks performed by building managers. In order to formalize various types of maintenance work procedures, a BIM-based maintenance process model is presented in two categories: inspection and maintenance. Among the BIM attribute data of the BIM model generated at the design phase, the parameters corresponding to the maintenance necessary information for each basic process are derived. Based on this, we proposed a COBie document prototype consisting of seven spreadsheets. The results of a case study confirmed that the KBIMS library-based BIM model created at the design phase without the maintenance information can be used at the maintenance phase.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.121-133
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• 2013

Defect-free transfer service on the Next-generation wireless network extensive roaming mobile node (MN) to provide efficient mobility management has become very important. MIPv6(Mobility IPv6) is one of mobility management scheme proposed by IETF(Internet Engineering Task Force), and IPv6-based mobility management techniques have been developed in various forms. One of each management techniques, IPv6-based mobility management techniques for PMIPv6 (MIPv6) system to improve the performance of a variety of F-PMIPv6 (Fast Handover for Proxy MIPv6) is proposed. However, the F-PMIPv6 is cannot be excellent than PMIPv6 in all scenarios. Therefor, to select a proper mobility management scheme between PMIPv6 and F-PMIPv6 becomes an interesting issue, for its potenrials in enhancing the capacity and scalability of the system. In this paper, we develop an analytical model to analyze the applicability of PMIPv6 and F-PMIPv6. Based on this model, we design an Secure Smart Mobility Support(SSM) scheme that selects the better alternative between PMIPv6 and F-PMIPv6 for a user according to its changing mobility and service characteristics. When F-PMIPv6 is adopted, SSM chooses the best mobility anchor point and regional size to optimize the system performance. Numerical results illustrate the impact of some key parameters on the applicability of PMIPv6 and F-PMIPv6. Finally, SSM has proven even better result than PMIPv6 and F-PMIPv6.

• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.67-78
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• 2022

Interest in bio-signal monitoring of wearable devices is increasing significantly as the next generation needs to develop new devices to dominate the global market of the information and communication technology industry. Accordingly, this research developed a resistive textile strain sensor through a wetting process in a single-wall carbon nanotube dispersion solution using an E-Band with low hysteresis. To measure the resistance signal in the E-Band to which electrical conductivity is applied, a universal material tester, an Arduino, and LCR meters that are microcontroller units were used to measure the resistance change according to the tensile change. To effectively handle various noises generated due to the characteristics of the fabric textile strain sensor, the filter performance of the sensor was evaluated using the moving average filter, Savitsky-Golay filter, and intermediate filters of signal processing. As a result, the reliability of the filtering result of the moving average filter was at least 89.82% with a maximum of 97.87%, and moving average filtering was suitable as the noise filtering method of the textile strain sensor.