• Journal of the Korea Convergence Society
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• v.7 no.1
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• pp.9-15
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• 2016

In this paper, we describe the design of a high speed data acquisition system(DAS) with STM32 processor based on Cortex-M4. The system is used for the sensor devices to collect raw data on production lines at factory and send them to the servo computer in real time. The system is designed for multi functions with universal asynchronous receiver and transmitter(UART), analog to digital converter(ADC), digital to analog converter(DAC), and general purpose input output(GPIO). those are well tested for various data acquisition and high speed motor control in real time.

• International journal of advanced smart convergence
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• v.5 no.2
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• pp.24-37
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• 2016

This paper proposes to increase the efficiency of energy in nodes, which rapidly drops during the transmission of the Low Energy Adaptive Clustering Hierarchy (LEACH), through the use of dual-hop layered application in the sensor field. Along with introducing the dual-hop method in the data transmission, the proposed single-hop method for short-range transmission and multi-hop transmission method between the cluster heads for remote transmission were also introduced. Additionally, by introducing a partial multi-hop method in the data transmission, a single-hop method for short range transmission method between the cluster heads for remote transmission was used. In the proposed DL-LEACH, the energy consumption of the cluster head for remote transmission reduced, as well as increased the energy efficiency of the sensor node by reducing the transmission distance and simplifying the transmission route for short-range transmission. As compared the general LEACH, it was adapted to a wider sensor field.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.112-118
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• 2018

This paper describes the design and implementation of a vehicle internal alarm system using raspberry-pie and gas sensor. It provides a notification system for sleepiness during driving, a driving time notification system and a smoking detection system. We coded using 'Python'. And we use 'MySQL' and 'PHP' to build the necessary servers and web pages for gathering sensing data and monitoring. The developed system was tested by several methods. All experiments showed satisfactory response signals and detected with immediate responses.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1065-1071
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• 2023

In this paper discuss with research developing an LSTM model for classifying the behavior of occupants within a residence. The multi-sensor consists of an IAQ (Indoor Air Quality) sensor that measures indoor air quality, a UWB radar that tracks occupancy detection and location, and a Piezo sensor to measure occupants' biometric information, and collects occupant behavior data such as going out, staying, cooking, cleaning, exercise, and sleep by constructed an experimental environment similar to the actual residential environment. After the data with removed outliers and missing, the LSTM model is used to calculate accuracy, sensitivity, specificity of the occupant behavior classification model, T1 score.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.29-38
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• 2022

This paper proposes a system for classifying gait types using an ensemble deep learning network for gait data measured by a smart insole equipped with multi-sensors. The gait type classification system consists of a part for normalizing the data measured by the insole, a part for extracting gait features using a deep learning network, and a part for classifying the gait type by inputting the extracted features. Two kinds of gait feature maps were extracted by independently learning networks based on CNNs and LSTMs with different characteristics. The final ensemble network classification results were obtained by combining the classification results. For the seven types of gait for adults in their 20s and 30s: walking, running, fast walking, going up and down stairs, and going up and down hills, multi-sensor data was classified into a proposed ensemble network. As a result, it was confirmed that the classification rate was higher than 90%.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.447-458
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• 2012

We present a method to recognize hand gestures using skeletal joint data obtained from Microsoft's Kinect sensor. We propose a combination feature of multi-angle histograms robust to orientation variations to represent the observation sequence of skeletons. The proposed feature efficiently represents the orientation variations of gestures that can be occurred according to person or environment by combining the multiple angle histograms with various angular-quantization levels. The gesture represented as combination of multi-angle histograms and random decision forest classifier improve the recognition performance. We conduct the experiments in hand gesture dataset obtained from a kinect sensor and show that our method outperforms the other methods by comparing the recognition performance.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.265-272
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• 2014

This study suggests a system for preventing and coping with diverse industrial accidents available for taking place in the industrial field by designing a context-aware system of the solar cell equipment with the use of multi-sensor. It installs multi-sensor in the surrounding and major positions of the solar cell equipment, acquires data on the surrounding situations and solar cell equipment from this device, and then save it into the local memory and transmits it to the server. The saved data recognizes a situation based on the context-aware algorithm and judges depending on the perceived result. An administrator comes to have environment available for monitoring the status on the production field and equipments with real time according to the judged outcome through the context-aware algorithm. The context-aware system in the industrial field, which is put today in the ubiquitous environment, will become a service of offering appropriate information for dealing with industrial accidents through real-time inspection.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.135-141
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• 2018

Wireless sensor networks (WSN) are composed of a large number of sensor nodes. Battery-powered sensor nodes have limited coverage; therefore, it is more efficient to transmit data via multi-hop communication. The network lifetime is a crucial issue in WSNs and the multi-hop routing protocol should be designed to prolong the network lifetime. Prolonging the network lifetime can be achieved by minimizing the power consumed by the nodes, as well as by balancing the power consumption among the nodes. A power imbalance can reduce the network lifetime even if several nodes have sufficient (battery) power. In this paper, we propose a routing protocol that prolongs the network lifetime by balancing the power consumption among the nodes. To improve the balance of power consumption and improve the network lifetime, the proposed routing scheme adaptively controls the transmission range using a power control according to the residual power in the nodes. We developed a routing simulator to evaluate the performance of the proposed routing protocol. The simulation results show that the proposed routing scheme increases power balancing and improves the network lifetime.

• ETRI Journal
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• v.36 no.6
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• pp.913-923
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• 2014

This paper proposes a global mapping algorithm for multiple robots from an omnidirectional-vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas-Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots. The multi-robot mapping algorithm draws a global map by using map data obtained from all of the individual robots. Global mapping takes a long time to process because it exchanges map data from individual robots while searching all areas. An omnidirectional image sensor has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. The process calculations of the correction algorithm are improved over existing methods by correcting only the object's feature points. The proposed algorithm has two steps: first, a local map is created based on an omnidirectional-vision SLAM approach for individual robots. Second, a global map is generated by merging individual maps from multiple robots. The reliability of the proposed mapping algorithm is verified through a comparison of maps based on the proposed algorithm and real maps.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.258-262
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• 2009

A Practical application of environmental monitoring system based on wireless sensor node network with the core of embedded system STR711FR2 microprocessor is presented in the paper. The adaptable and classifiable wireless sensor node network is used to achieve the data acquisition and multi-hop wireless communication of parameters of the monitoring base station environment including repeaters. The structure of the system is proposed and the hardware architecture of the system is designed, and the system operating procedures is proposed. As a result of field test, designed hardware platform operated with 50kbps bit rate and 5MHz channel spacing at 2040Hz. The wireless monitoring system can be managed and swiftly retreated without support of base station environmental monitoring.