• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.17-26
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• 2018

In this paper, we propose a gait type classification method based on pressure sensor which reflects various terrain and velocity variations. In order to obtain stable gait classification performance, we divide the whole gait data into several steps by detecting the swing phase, and normalize each step. Then, we extract robust features for both topographic variation and speed variation by using the Null-LDA(Null-Space Linear Discriminant Analysis) method. The experimental results show that the proposed method gives a good performance of gait type classification even though there is a change in the gait velocity and the terrain.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.75-83
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• 2018

Predictive IoT Sensor Algorithm is a technique of data science that helps computers learn from existing data to predict future behaviors, outcomes, and trends. This algorithm is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Sensors and computers collect and analyze data. Using the time series prediction algorithm helps to predict future temperature. The application of this IoT in industrial environments like power plants and factories will allow organizations to process much larger data sets much faster and precisely. This rich source of sensor data can be networked, gathered and analyzed by super smart software which will help to detect problems, work more productively. Using predictive IoT technology - sensors and real-time monitoring - can help organizations exactly where and when equipment needs to be adjusted, replaced or how to act in a given situation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.133-134
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• 2019

스마트 센서는 센싱 기능 외에 통신, 데이터 처리, 의사결정 기능을 추가로 갖추고, 수집한 데이터를 바탕으로 자체적으로 판단하고 처리하는 기능을 포함된 지능적 센서이다. 실생활에도 밀접한 연관을 가질 수 있고 범위 또한 점차 확대되어가고 있다. 본 논문에서는 센싱 데이터의 IoT 활용과 맞물려 스마트 센싱 데이터의 다양하고 새로운 활동 분야에 대해서 탐색하고자 한다.

• Current Optics and Photonics
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• v.7 no.2
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• pp.200-206
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• 2023

Near-infrared (NIR) sensing technology using CMOS image sensors is used in many applications, including automobiles, biological inspection, surveillance, and mobile devices. An intuitive way to improve NIR sensitivity is to thicken the light absorption layer (silicon). However, thickened silicon lacks NIR sensitivity and has other disadvantages, such as diminished optical performance (e.g. crosstalk) and difficulty in processing. In this paper, a pixel structure for NIR sensing using a stacked CMOS image sensor is introduced. There are two photodetection layers, a conventional layer and a bottom photodiode, in the stacked CMOS image sensor. The bottom photodiode is used as the NIR absorption layer. Therefore, the suggested pixel structure does not change the thickness of the conventional photodiode. To verify the suggested pixel structure, sensitivity was simulated using an optical simulator. As a result, the sensitivity was improved by a maximum of 130% and 160% at wavelengths of 850 nm and 940 nm, respectively, with a pixel size of 1.2 ㎛. Therefore, the proposed pixel structure is useful for NIR sensing without thickening the silicon.

• The KIPS Transactions:PartC
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• v.14C no.3 s.113
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• pp.239-254
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• 2007

Ubiquitous computing supports environment to freely connect to network without restrictions of place and time. This environment enables easy access and sharing of information, but because of easy unauthorized accesses, specified security policy is needed. Especially, ubiquitous sensor network devices use limited power and are small in size, so, many restrictions on policies are bound to happen. This paper proposes double-key based light weight security protocol, independent to specific sensor OS, platform and routing protocol in ubiquitous sensor network. The proposed protocol supports safe symmetric key distribution, and allows security manager to change and manage security levels and keys. This had a strong merit by which small process can make large security measures. In the performance evaluation, the proposed light weight security protocol using double-key in ubiquitous sensor network allows relatively efficient low power security policy. It will be efficient to ubiquitous sensor network, such as smart of ace and smart home.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.3
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• pp.268-274
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• 2004

Patch type piezo-polymer sensors for smart structures were experimented to detect elastic wave. The pencil lead braking test was performed to analyze the characteristics of patch-type piezo-polymer sensors such as polyvinyliden fluoride (PVDF) and polyvinylidene fluoride trifluorethylene (P(VDF-TrFE)) for several test specimens with various elastic wave velocities and acoustical impedances. The characteristics of the patch-type piezo-polymer sensor were compared with the commercial PZT acoustic emission (AE) sensor. The vacuum grease and epoxy resin were used as a couplant for the acoustic impedance matching between the sensor and specimen. The peak amplitude of elastic wave increased as the diameter of piezo-film and acoustical impedance of the specimen increased. The frequency detection range of the piezo-film sensors decreased with increasing diameter of the piezo-film sensor. The P(VDF-TrFE) sensor was more sensitive than the PVDF sensor.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.109-114
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• 2024

In this paper, we design and implement a worker motion 3D visualization module based on human sensors. The three key modules that make up this system are Human Sensor Implementation, Data Set Creation, and Visualization. Human Sensor Implementation provides the functions of setting and installing the human sensor locations and collecting worker motion data through the human sensors. Data Set Creation offers functions for converting and storing motion data, creating near real-time worker motion data sets, and processing and managing sensor and motion data sets. Visualization provides functions for visualizing the worker's 3D model, evaluating motions, calculating loads, and managing large-scale data. In worker 3D model visualization, motion data sets (Skeleton & Position) are synchronized and mapped to the worker's 3D model, and the worker's 3D model motion animation is visualized by combining the worker's 3D model with analysis results. The human sensor-based worker motion 3D visualization module designed and implemented in this paper can be widely utilized as a foundational technology in the smart factory field in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.650-661
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• 2020

The digital transformation of the 4th industrial revolution is leading to changes and innovations in the global economy. Various countries are focusing on reviving their manufacturing industries and economic recovery through smart factories. The purpose of this study is to empirically identify technological determinants for the successful implementation of the smart factory and to verify teose effects on manufacturing operations and the firms' operational/environmental performances. Five factors, including sensor network, platform technology, information system, intelligent automation, and safety, were defined as core technologies. The SEM analysis results of 157 small and medium-sized manufacturing firms that have implemented smart factories are as follows. First, sensor network, platform technology, and information system had significant effects on smart manufacturing operations. Second, smart manufacturing operations have improved firm performance. This study is valuable in that it has confirmed the effectiveness of government-funded projects and systemized key technologies for implementing smart factories. Meanwhile, it is helpful for practitioners to support an efficient and effective decision-making for the new adoption.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.576-582
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• 2016

Currently, every person possesses a smart phone due to the development of the IT industry. However, crosswalk pedestrian accidents have been sharply increasing due to smart phone use. If a traffic light can recognize smart phones when a smart-phone user approaches and arrives at a given sign, many accidents could be reduced by using beacon signals. Before the era of smart phones, the accident rate involving cell phone use was relatively low. Nevertheless, when considering the development of IT equipment that produces a threat to human life, government cannot regulate smart phone use outside. The purpose of this paper is to indirectly warn a smart phone user in order to reduce the accident rates.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.475-480
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• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.