• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.120-126
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• 2020

Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.321-322
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• 2022

Recently, in the field of health care, health management topics are being dealt with a lot. In particular, services are concentrated in the field of exercise, and many exercise equipment fused with the 4th industry-based technology are also appearing. In general, a form of collecting user's exercise information by mounting various sensors on exercise equipment and linking services based on this is representative. In this paper, we propose an efficient data transmission based on the design in the process of transmitting the exercise data generated by the equipment to the smartphone through the Bluetooth-based IoT sensor and transmitting it back to the exercise information analysis platform.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1235-1242
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• 2008

In this paper, x-ray image sensor of photon counting type having a $32{\times}32$ pixel array is designed with $0.18{\mu}m$ triple-well CMOS process. Each pixel of the designed image sensor has an area of loot $100{\times}100\;{\mu}m2$ and is composed of about 400 transistors. It has an open pad of an area of $50{\times}50{\mu}m2$ of CSA(charge Sensitive Amplifier) with x-ray detector through a bump bonding. To reduce layout size, self-biased folded cascode CMOS OP amp is used instead of folded cascode OP amp with voltage bias circuit at each single-pixel CSA, and 15-bit LFSR(Linear Feedback Shift Register) counter clock generator is proposed to remove short pulse which occurs from the clock before and after it enters the counting mode. And it is designed that sensor data can be read out of the sensor column by column using a column address decoder to reduce the maximum current of the CMOS x-ray image sensor in the readout mode.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.1
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• pp.43-56
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• 2012

Wireless sensor networks consist of numerous small-sized nodes equipped with limited computing power and storage as well as energy-limited disposable batteries. In this networks, nodes are deployed in a large given area and communicate with each other in short distances via wireless links. For energy efficient networks, dynamic clustering protocol is an effective technique to achieve prolonged network lifetime, scalability, and load balancing which are known as important requirements. this technique has a characteristic that sensing data which gathered by many nodes are aggregated by cluster head node. In the case of cluster head node is exposed by attacker, there is no guarantee of safe and stable network. Therefore, for secure communications in such a sensor network, it is important to be able to encrypt the messages transmitted by sensor nodes. Especially, cluster based sensor networks that are designed for energy efficient, strongly recommended suitable key management and authentication methods to guarantee optimal stability. To achieve secured network, we propose a key management scheme which is appropriate for hierarchical sensor networks. Proposed scheme is based on polynomial key pool pre-distribution scheme, and sustain a stable network through key authentication process.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.59-67
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• 2005

Building information is primary source in many applications such as mapping, telecommunication, car navigation and virtual city modeling. While aerial CCD images which are captured by passive sensor(digital camera) provide horizontal positioning in high accuracy, it is far difficult to process them in automatic fashion due to their inherent properties such as perspective projection and occlusion. On the other hand, LIDAR system offers 3D information about each surface rapidly and accurately in the form of irregularly distributed point clouds. Contrary to the optical images, it is much difficult to obtain semantic information such as building boundary and object segmentation. Photogrammetry and LIDAR have their own major advantages and drawbacks for reconstructing earth surfaces. The purpose of this investigation is to automatically obtain spatial information of 3D buildings by fusing LIDAR data with aerial CCD image. The experimental results show that most of the complex buildings are efficiently extracted by the proposed method and signalize that fusing LIDAR data and aerial CCD image improves feasibility of the automatic detection and extraction of buildings in automatic fashion.

• The Journal of the Acoustical Society of Korea
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• v.36 no.1
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• pp.39-48
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• 2017

CDMA (Code Division Multiple Access) is promising medium access control schemes for underwater acoustic sensor networks because of its robustness against frequency-selective fading and high frequency-reuse efficiency. In this paper, we design diversity schemes of underwater CDMA transceiver for the forward and reverse links. User data are multiplexed by Walsh code and a pseudo random noise code acquisition process is added for phase error correction before decoding the user data at the receiver. Then, the diversity reception using equal gain combining and maximal ratio combining is performed in order to minimize performance degradation caused by rich multipath fading of underwater acoustic channel. We evaluated the performance of diversity transceiver through lake experiment, which was performed at Lake Kyungcheon, Mungyeong city using two transmitters and two receivers placed 460 m apart at an average depth of 40 m. The lake experiment results show that user data are recovered with error-free in both of the forward and reverse links.

• Korean Journal of Construction Engineering and Management
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• v.24 no.3
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• pp.42-51
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• 2023

Freeze and burst water meter in winter causes many social costs, such as meter replacement cost, inability of water use, and secondary damage by freezing water. The government is making efforts to modernize local waterworks, and in particular, is promoting SWM(Smart Water Management) project nationwide. In this study suggests a new freeze risk notification information service based on the temperature by IoT sensor inside the water meter box rather than outside temperature. In addition, in order to overcome the quantitative and regional limitation of IoT temperature sensors installed nationwide, and AI based temperature prediction model was developed that predicts the temperature inside water meter boxes based on data acquired from IoT temperature sensors and other information. Through the prediction model optimization process, a nationwide water meter freezing risk information service was convinced.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2449-2452
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• 2007

Hot spots on thin wafers of IC packages are becoming important issues in thermal and electrical engineering fields. To investigate these hot spots, we developed a Diode Temperature Sensor Array (DTSA) that consists of an array of 32 ${\times}$32 diodes (1,024 diodes) in a 8 mm ${\times}$ 8 mm surface area. To know specifically the hot spot temperature which is affected by the chip thickness and a generated power, we made the DTSA chips, which have 21.5 ${\mu}m$, 31 ${\mu}m$, 42 ${\mu}m$, 100 ${\mu}m$, 200 ${\mu}m$, and 400 ${\mu}m$ thickness using the CMP process. And we conducted the experiment using various heater power conditions (0.2 W, 0.3 W, 0.4 W, 0.5 W). In order to validate experimental results, we performed a numerical simulation. Errors between experimental results and numerical data are less than 4%. Finally, we proposed a correlation for the hot spot temperature as a function of the generated power and the wafer thickness based on the results of the experiment. This correlation can give an easy estimate of the hot spot temperature for flip chip packaging when the wafer thickness and the generated power are given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3621-3640
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• 2016

In the process of k-coverage of the target node, there will be a lot of data redundancy forcing the phenomenon of congestion which reduces network communication capability and coverage, and accelerates network energy consumption. Therefore, this paper proposes a novel energy balanced k-coverage control algorithm based on probability model (EBKCCA). The algorithm constructs the coverage network model by using the positional relationship between the nodes. By analyzing the network model, the coverage expected value of nodes and the minimum number of nodes in the monitoring area are given. In terms of energy consumption, this paper gives the proportion of energy conversion functions between working nodes and neighboring nodes. By using the function proportional to schedule low energy nodes, we achieve the energy balance of the whole network and optimizing network resources. The last simulation experiments indicate that this algorithm can not only improve the quality of network coverage, but also completely inhibit the rapid energy consumption of node, and extend the network lifetime.