• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.76-88
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• 2019

With advances in olfactory sensor technologies, the number of reports on various intelligent applications using multiple sensors (sensor arrays) are continuously increasing for fields such as medicine, environment, security, etc. For intelligent and point-of-care applications, it is not only important for the sensor technology to perform chemical or physical measurements rapidly and accurately, but it is also important for artificial intelligence technology to recognize and quantify specific chemicals or diagnose diseases such as lung cancer and diabetes. In particular, great advances in pattern recognition technologies, including deep learning algorithms, as well as sensor array technologies, are expected to enhance the potential of various types of olfactory intelligence applications, including early cancer diagnosis, drug seeking, military operations, and air pollution monitoring.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.373-374
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• 2019

2004년 이후로 무형유산의 중요성이 대두되면서 박물관의 설립 및 전시 콘텐츠의 수요는 증가하고 있으나, 그 콘텐츠를 활용한 ICT응용기술 발전이 미흡하였다. 본 논문에서는 국가무형유산중 하나인 봉산 탈춤을 기반으로 사용자의 상호작용이 가능한 교육형 인터랙션 콘텐츠 응용을 제안한다. 키넥트와 팔에 장착된 자이로 센서들을 활용한 향상된 동작 추적을 기반으로, 사용자는 봉산탈춤의 기본 동작 및 과정을 따라함으로써 봉산탈춤을 학습 할 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.71-75
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• 2021

Recently, transition-metal-based hydroxide materials have attracted significant attention in various electrochemical applications owing to their low cost, high stability, and versatility in composition and morphology. Among these applications, transition-metal-based hydroxides have exhibited significant potential in supercapacitors owing to their multiple redox states that can considerably enhance the supercapacitance performance. In this study, nanostructured Ni-Mn double hydroxide is directly grown on a conductive substrate using an electrodeposition method. Ni-Mn double hydroxide exhibits excellent electrochemical charge-storage properties in a 1 M KOH electrolyte, such as a specific capacitance of 1364 Fg-1 at a current density of 1 mAcm-2 and a capacitance retention of 94% over 3000 charge-discharge cycles at a current density of 10 mAcm-2. The present work demonstrates a scalable, time-saving, and cost-effective approach for the preparation of Ni-Mn double hydroxide with potential application in high-charge-storage kinetics, which can also be extended for other transition-metal-based double hydroxides.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.459-461
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• 2022

In this paper, we propose a measurement system for measuring radioactivity detected in an indoor environment. This is to measure and prevent radiation generated in various spaces such as general house, workplace, and research institutes. Multi-radioactivity sensors are used to measure multiple spaces simultaneously. The measured radioactivity data is transmitted to the PC in real time through ZigBee and monitored. Even with a small amount of radioactivity, it is considered that it must be installed in a place where radiation exposure is expected, such as a laboratory or workplace, for prevention from chronic radiation syndrome.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.159-160
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• 2022

본 논문에서는 최근 주요시설의 경계에 주로 사용되기 시작한 라이다 센서를 여러대 사용할때 보다 효율적으로 사용하기 위해서 통합된 3차원 좌표계에서 시각화하는 방법에 대해 설명한다. 주로 카메라 기반 CCTV의 경우 정확성은 높지만 시야각(Field of View)이 좁기 때문에 레이더(RADAR)센서와 같은 센서와 함께 혼용되는 경우가 많다. 레이더 센서의 데이터는 넓은 범위에 대한 감지를 할 수 있지만 노이즈가 많고 물체의 형상을 정확하게 측정하기 힘들다. 라이다(LiDAR) 센서는 레이져를 이용하여 멀고 넓은 범위를 정교하게 측정할 수 있다. 이러한 라이다 센서는 정교한 만큼 처리해야할 데이터의 양이 많으며, 다수의 센서를 이용하더라도 하나의 화면에서 처리하기 힘들다는 단점이 있다. 제안하는 논문은 여러개의 라이다 센서에서 측정한 데이터를 실시간에 하나의 좌표계로 통일하여 하나의 영상을 보일 수 있도록 통합 뷰잉 환경을 제공한다.

• ETRI Journal
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• v.45 no.2
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• pp.329-337
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• 2023

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.59-65
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• 2023

Currently, polysomnography is essential to diagnose sleep-related breathing disorders. However, there are several disadvantages to polysomnography, such as the requirement for multiple sensors and a long reading time. In this paper, we propose a system for predicting the severity of sleep-related breathing disorders at home utilizing measurable elements in a wearable device. To predict severity, the variables were refined through a three-step variable selection process, and the refined variables were used as inputs into three machine-learning models. As a result of the study, random forest models showed excellent prediction performance throughout. The best performance of the model in terms of F1 scores for the three threshold criteria of 5, 15, and 30 classified as the AHI index was about 87.3%, 90.7%, and 90.8%, respectively, and the maximum performance of the model for the three threshold criteria classified as the RDI index was approx 79.8%, 90.2%, and 90.1%, respectively.

• ETRI Journal
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• v.45 no.4
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• pp.627-635
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• 2023

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0-1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.41-47
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• 2012

In this paper, we proposed a new method which analyzes and eliminates errors occurring by multi-reflection of ultrasonic firing in mobile robot application. This new method allows ultrasonic sensors to fire at rates that are three times faster than those customary in conventional applications readings due to ultrasonic noise disturbance. It is possible them to collect and predict sensor data much faster than conventional methods. Furthermore, this method's capability allows mobile robot to navigate in a complex and unknown environment and to collaborate in the same environment with multiple mobile robot, even if their ultrasonic sensors operate. And it's usefulness to avoid moving obstacles by capability of rapid collecting data. Finally, we present experimental results that demonstrate the performances of the new proposed method by experiments in a multi-reflective environment.