• ETRI Journal
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• v.44 no.3
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• pp.426-437
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• 2022

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.

• Journal of Sensor Science and Technology
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• v.32 no.1
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• pp.16-21
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• 2023

This study reports the potential of using commercial copy papers as substrates for simple sensitive glucose detection. Typical paper-based devices use filter papers as porous substrates that can contain reagents; however, this is the first study to report the use of copy papers for the purpose of enhancing enzymatic colorimetric detection. Glucose detection using glucose oxidase, horseradish peroxidase and potassium iodide was performed on a copy paper, cellulose-based filter paper, and polyethylene film. The results indicated that the copy paper exhibited a stronger coloration than the other substrates. Reagents required for detection were dried on the copy paper, and a 3D-printed holder was designed to provide an environment for consistent imaging, making it a convenient cost-effective option for point-of-care testing using a mobile phone camera. The simple paper-based glucose sensor exhibited a linear range of 0.1-20 mM, limit of quantification of 0.477 mM, and limit of detection of 0.143 mM.

• Journal of Powder Materials
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• v.30 no.4
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• pp.310-317
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• 2023

Small-film-type ion sensors are garnering considerable interest in the fields of wearable healthcare and home-based monitoring systems. The performance of these sensors primarily relies on electrode capacitance, often employing nanocomposite materials composed of nano- and sub-micrometer particles. Traditional techniques for enhancing capacitance involve the creation of nanoparticles on film electrodes, which require cost-intensive and complex chemical synthesis processes, followed by additional coating optimization. In this study, we introduce a simple one-step electrochemical method for fabricating gold nanoparticles on a carbon nanotube (Au NP-CNT) electrode surface through cyclic voltammetry deposition. Furthermore, we assess the improvement in capacitance by distinguishing between the electrical double-layer capacitance and diffusion-controlled capacitance, thereby clarifying the principles underpinning the material design. The Au NP-CNT electrode maintains its stability and sensitivity for up to 50 d, signifying its potential for advanced ion sensing. Additionally, integration with a mobile wireless data system highlights the versatility of the sensor for health applications.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.267-274
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• 2023

LiDAR, one of the most important sensing methods used in mobile robots and cars with assistive/autonomous driving functions, is used to locate surrounding obstacles or to build maps. For real-time path generation, the detection of potholes or puddles on the driving surface is crucial. To achieve this, we used the coordinates of the reflection points provided by LiDAR as well as the intensity information to classify water areas, which was achieved by applying a linear regression method to the intensity distribution. The rationale for using the LiDAR index as an input variable for linear regression is presented, and we demonstrated that it is not affected by errors in the distance measurement value. Because of LiDAR vertical scanning, if the reflective surface is not uniform, it is divided into different groups according to the intensity distribution, and a mathematical basis for this is presented. Through experiments in an outdoor driving area, we could distinguish between flat ground, potholes, and puddles, and kinematic analysis was performed to calculate the maximum width that could be crossed for a given vehicle body size and wheel radius.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.25-37
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• 2024

The objective of this study was to create intelligent rail robots that are optimized for facility management and implement digital twin systems for smart road tunnel management. An autonomous surveillance system is formed by combining the sensing platform consisting of railing robots, fixed cameras and environmental detection sensors with the digital twin data platform technology for tunnel monitoring and early fire suppression. In order to develop mobile rail robots for fire extinguishing, we also designed and manufactured robots for extinguishing & monitoring and fire extinguishing devices, and then we examined the optimization of all parts. Our next step was to build a digital twin for road tunnel management by developing continuous image display system and implementing 3D modeling. After constructing prototypes, we attempted simulations by configuring abnormal symptom scenarios, such as vehicles fires. This study's proposal proposes high-accuracy risk prediction services that will enable intelligent management of risks in the tunnel with early response at each stage, using the data collected from the intelligent rail robots and digital twin systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.5
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• pp.271-280
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• 2014

In wireless sensor networks, there have been two methods for sensing continuously moving object tracking: user-query based method and periodic report based method. Although the former method requires overhead for user query rather than the latter method, the former one is known as an energy-efficient method without transferring unnecessary information. In the former method, a virtual tree, consisting of sensor nodes, is exploited for the user querying and sensor reporting. The tree stores the information about mobile objects; the stored information is triggered to report by the user query. However, in case of fast moving object, the tracking accuracy reduces due to the time delay of end-to-end repeated query. To solve the problem, we propose a query relaying method reducing the time delay for mobile object tracking. In the proposed method, the nodes in the tree relay the query to the adjacent node according to the movement of mobile object tracking. Relaying the query message reduces the end-to-end querying time delay. Simulation results show that our method is superior to the existing ones in terms of tracking accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.7
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• pp.630-642
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• 2013

To transmit vital signal stream of mobile patients remotely, it requires mobility of patient and watcher, sensing function of patient's abnormal symptom and self-organizing service binding of related computing resources. In the existing relative researches, the vital signal stream is transmitted as a centralized approach which exposure the single point of failure itself and incur data traffic to central server although it is localized service. Self-organizing middleware platform based on heterogenous overlay network is a middleware platform which can transmit real-time data from sensor device(including vital signal measure devices) to Smartphone, TV, PC and external system through overlay network applied self-organizing mechanism. It can transmit and save vital signal stream from sensor device autonomically without arbitration of management server and several receiving devices can simultaneously receive and display through interaction of nodes in real-time.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.149-157
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• 2019

Recent research has been actively carried out on systems that want to optimize resource utilization by analyzing the intended behavior and pattern of behavior of objects (users, consumers). A service system that applies information about an object's location or behavior must include a location trigger processing system for tracking an object's real-time location. In this paper, we analyze design problems for the implementation of a context-awareness based location trigger system, and present system models based on analysis details. For this purpose, this paper introduces the concept of location trigger for intelligent location tracking techniques about moving situations of objects, and suggests a mobile agent system with active rules that can perform monitoring and appropriate actions based on sensing information and location context information, and uses them to design and implement the location trigger system for context-awareness based location services. The proposed system is verified by implementing location trigger processing scenarios and trigger service and action service protocols. In addition, through experiments on mobile agents with active rules, it is suggested that the proposed system can optimize the role and function of the application system by using rules appropriate to the service characteristics and that it is scalable and effective for location-based service systems. This paper is a preliminary study for the establishment of an optimization system for utilizing resources (equipment, power, manpower, etc.) through the active characteristics of systems such as real-time remote autonomous control and exception handling over consumption patterns and behavior changes of power users. The proposed system can be used in system configurations that induce optimization of resource utilization through intelligent warning and action based on location of objects, and can be effectively applied to the development of various location service systems.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.1-31
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• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.