• Journal of the Society of Disaster Information
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• v.12 no.2
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• pp.122-129
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• 2016

In this study, a dual structured pipe for a preventive monitoring on a accident damage, a smartphone app program for a survey and a construction data collection and a server program for a real-time monitoring were developed. A pilot system was installed in the test field to analyze effects of a system developed in this study. The data for a damage prevention was detected by the attached sensors on the pipe. Exclusion was tested by the pressure sensors to be installed at regular intervals. The app and server programs was enabled to the real-time data collection and real-time monitoring linked by VRS survey equipments and a smartphone.

• Korean Journal of Optics and Photonics
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• v.29 no.4
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• pp.149-158
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• 2018

A generalized structural design equation can be used to simplify and systematize a telecentric lens system composed of multiple lenses, as a creative design method of the authors. Through this structural equation, we have investigated the feasibility and design methodology of a telecentric lens equipped with a conventional smartphone camera for machine vision. As a result, we could verify and present a useful, generalized structural equation termed the $f{\theta}$ formula, being able to divide and combine the whole telecentric lens system into two modularized lens groups.

• Journal of Korea Multimedia Society
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• v.15 no.8
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• pp.1059-1066
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• 2012

With the development of Smartphone, Smartphone contains diverse functions including many sensors that can describe users' state. So there has been increased studies rapidly about activity recognition and life pattern recognition with Smartphone sensors. This research suggest modeling of the activity data to classify extracted data in existing activity recognition study. Activity data is divided into two parts: Physical activity and Logical Activity. In this paper, activity data modeling is theoretical analysis. We classified the basic activity(walking, standing, sitting, lying) as physical activity and the other activities including object, target and place as logical activity. After that we suggested a method of visualizing modeling data for users. Our approach will contribute to generalize human's life by modeling activity data. Also it can contribute to visualize user's activity data for existing activity recognition study.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.811-819
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• 2014

Activity recognition is a key component in identifying the context of a user for providing services based on the application such as medical, entertainment and tactical scenarios. Instead of applying numerous sensor devices, as observed in many previous investigations, we are proposing the use of smartphone with its built-in multimodal sensors as an unobtrusive sensor device for recognition of six physical daily activities. As an improvement to previous works, accelerometer, gyroscope and magnetometer data are fused to recognize activities more reliably. The evaluation indicates that the IBK classifier using window size of 2s with 50% overlapping yields the highest accuracy (i.e., up to 99.33%). To achieve this peak accuracy, simple time-domain and frequency-domain features were extracted from raw sensor data of the smartphone.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.182-196
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• 2024

This study introduces driving pattern analysis and change detection methods using smartphone sensors, based on contrastive learning. These methods characterize driving patterns without labeled data, allowing accurate classification with minimal labeling. In addition, they are robust to domain changes, such as different vehicle types. The study also examined the applicability of these methods to smartphones by comparing them with six lightweight deep-learning models. This comparison supported the development of smartphone-based driving pattern analysis and assistance systems, utilizing smartphone sensors and contrastive learning to enhance driving safety and efficiency while reducing the need for extensive labeled data. This research offers a promising avenue for addressing contemporary transportation challenges and advancing intelligent transportation systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.431-432
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• 2018

Humans have used acceleration sensors to perceive behavior. Using the application principles of inertia sensor such as acceleration and gyro sensors, the sensor indirectly measures movement through forces applied on a three-dimensional axis. This may result in different results depending on the location of the sensor in possession, even in the same way. The sensor, which is also mounted on popular smartphones, measured different values of acceleration sensors, whether in hand or in pocket, which are commonly used by people.

• Journal of KIISE:Software and Applications
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• v.41 no.8
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• pp.586-597
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• 2014

The next generation mobile computing technology is recently attracting attention that smartphone and wearable device imbedded with various sensors are being deployed in the world. Existing activity and posture recognition research can be divided into two different ways considering feature of one's movement. While activity recognition focuses on catching distinct pattern according to continuous movement, posture recognition focuses on sudden change of posture and body orientation. There is a lack of research constructing a system mixing two separate patterns which could be applied in real world. In this paper, we propose a method to use both smartphone and wearable device to recognize activity and posture in the same time. To use smartphone and wearable sensor data together, we designed a pre-processing method and constructed recognition model mixing signal vector magnitude and orientation pattern features of vertical and horizontal. We considered cycling, fast/slow walking and running activities, and postures such as standing, sitting, and laying down. We confirmed the performance and validity by experiment, and proved the feasibility in real world.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.19-26
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• 2018

Social issues for environmental pollution are continuously increasing globally. Especially, Users require services to measure environmental factors in indoor and outdoor and manage related data effectively and conveniently. According to this demand, sensors that can measure environmental factors in indoor and outdoor have been developed. However, since one sensor is composed of independent module, the interface of output data from each sensor is different. To solve the problem, we propose a customized protocol for low-power short-range wireless communication between smartphone using Bluetooth and All-in-One sensor device board and analyze the performance of the proposed customized protocol by developing program for performance verification of interface with user smartphone through Bluetooth. In addition, we implement a smartphone application using proposed protocol.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.37-43
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• 2020

Smartphone-based localization has been widely studied in many different scenarios. But as far as we know, few work has addressed the problem of localization in underground public transportation systems, where GPS signal and wireless infrastructure are not always available. Knowing the location of a train is necessary to develop a useful service for subway passengers. And so, estimation of motion state and stop station by using sensors on a smartphone is being studied for subway passengers. This paper proposes a localization method that uses a barometer and a magnetic sensor on a smartphone. The method proposed in this paper first estimates whether the train runs or stops according to the change in air pressure and the strength of the magnetic field. The altitude value and the magnetic field value are then used to estimate the exact stop station of the train. We evaluated the proposed method using data from the Seoul's subway line 5. Compared with previous methods, the proposed method achieves higher accuracy.

• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.72-81
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• 2021

We developed smartphone application and web application server to acquire and effectively manage tree measurement information. Smartphone applications can measure tree height, diameter at breast height (DBH), azimuth, altitude, slope, and positional coordinates using augmented reality (Google AR core) and motion sensors. The web application server effectively manages and stores measurement information. To evaluate the accuracy of information acquired using a smartphone, 90 Korean pine trees (Pinus koraiensis) were randomly selected from a natural mixed forest, with a total of 90 representative trees randomly collected from a natural mixed forest. Then, height and DBH were measured using a Haglof Vertex Laser Hypsometer and caliper. Comparisons of the results indicated significant results at the 95% level and a very high average correlation of 0.972 for both tree height and DBH. In terms of DBH, the average errors were 0.6745 cm and 1.0139 cm for artificial coniferous and natural mixed forests, respectively.