• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.199-206
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• 2013

An emergency monitoring system for the elderly, which uses acceleration data measured with an accelerometer, angular velocity data measured with a gyroscope, and heart rate measured with an electrocardiogram, is proposed. The proposed fall detection algorithm uses multiple parameter combinations in which all parameters, calculated using tri-axial accelerations and bi-axial angular velocities, are above a certain threshold within a time period. Further, we propose an emergency detection algorithm that monitors the movements of the fallen elderly person, after a fall is detected. The results show that the proposed algorithms can distinguish various types of falls from activities of daily living with 100% sensitivity and 98.75% specificity. In addition, when falls are detected, the emergency detection rate is 100%. This suggests that the presented fall and emergency detection method provides an effective automatic fall detection and emergency alarm system. The proposed algorithms are simple enough to be implemented into an embedded system such as 8051-based microcontroller with 128 kbyte ROM.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.370-375
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• 2023

As we enter a super-aged society, studies are being conducted to reduce complications and deaths caused by falls in elderly adults. Research is being conducted on interventions for preventing falls in the elderly, wearable devices for detecting falls, and methods for improving the performance of fall detection algorithms. Wearable devices for detecting falls of the elderly generally use gyro sensors. In addition, to improve the performance of the fall detection algorithm, an artificial intelligence algorithm is applied to the x, y, z coordinate data collected from the gyro sensor. In this paper, we develop a wearable device that uses a gyro sensor, body temperature, and heart rate sensor for health management as well as fall detection for the elderly. In addition, we develop a fall detection and health management system that works with wearable devices and a guardian's mobile app to improve the performance of the fall detection algorithm and provide health information to guardians.

• ETRI Journal
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• v.29 no.2
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• pp.143-152
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• 2007

In this paper, we propose a time-varying modified minimum mean-squared error (MMSE) detector for the detection of higher data rate signals in a multirate asynchronous code-division multiple-access (CDMA) system which is signaled in a fast Rayleigh fading channel. The interference viewed by a higher data rate symbol will be periodic due to the presence of a lower data rate symbol which spans multiple higher data rate symbols. The detection is carried out on the basis of a modified MMSE criterion which incorporates differential detection and the ratio of channel coefficients in two consecutive observation intervals inherently compensating the fast variation of the channel due to fading. The numerical results obtained by the MMSE detector with time-varying detection show around 3 dB (M=2) and 6 dB (M=4) performance improvement at a BER of $10^{-3}$ in the AWGN channel, while introducing more computational complexity than the MMSE detector without time-varying detection. At a higher $E_b/N_0$, the proposed scheme can achieve a BER of approximately $10^{-3}$ in the presence of fast channel variation which is an improvement over other schemes.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1839-1845
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• 2016

Detecting remote targets is important to active protection system (APS) or infrared search and track (IRST) applications. In normal situation, the well-known constant false alarm rate (CFAR) detector works properly. However, decoys in APS or closely spaced targets in IRST degrade the detection capability by increasing background noise level in the CFAR detector. This paper presents a context aware CFAR detector by the intensity sorting and selection of background region to reduce the effect of neighboring targets that lead to incorrect estimation of background statistics. The existence of neighboring targets can be recognized by intensity sorting where neighboring targets usually show highest ranks. The proposed background statistics (mean, standard deviation) estimation method from median local pixels can be aware of the background context and reduce the effects of the neighboring targets, which increase the signal-to-clutter ratio. The experimental results on the synthetic APS sequence, real adjacent target sequence, and remote pedestrian sequence validated that the proposed method produced an enhanced detection rate with the same false alarm rate compared with the hysteresis-CFAR (H-CFAR) detection.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.476-481
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• 2019

Early detection of fire is an important measure for minimizing the loss of life and property damage. However, fire and smoke need to be simultaneously detected. In this context, numerous studies have been conducted on image-based fire detection. Conventional fire detection methods are compute-intensive and comprise several algorithms for extracting the flame and smoke characteristics. Hence, deep learning algorithms and convolution neural networks can be alternatively employed for fire detection. In this study, recorded image data of fire in a ship engine room were analyzed. The flame and smoke characteristics were extracted from the outer box, and the YOLO (You Only Look Once) convolutional neural network algorithm was subsequently employed for learning and testing. Experimental results were evaluated with respect to three attributes, namely detection rate, error rate, and accuracy. The respective values of detection rate, error rate, and accuracy are found to be 0.994, 0.011, and 0.998 for the flame, 0.978, 0.021, and 0.978 for the smoke, and the calculation time is found to be 0.009 s.

• Journal of IKEEE
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• v.22 no.4
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• pp.1079-1087
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• 2018

Recently, computer vision field based deep learning artificial intelligence has become a hot topic among various image analysis boundaries. In this study, flames are detected in fire images using the Faster R-CNN algorithm, which is used to detect objects within the image, among various image recognition algorithms based on deep learning. In order to improve fire detection accuracy through a small amount of data sets in the learning process, we use image augmentation techniques, and learn image augmentation by dividing into 6 types and compare accuracy, precision and detection rate. As a result, the detection rate increases as the type of image augmentation increases. However, as with the general accuracy and detection rate of other object detection models, the false detection rate is also increased from 10% to 30%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.785-796
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• 2012

In this paper, we propose an effective Behavior-based detection technique using the frequency of system calls to detect malicious code, when the number of training data is fewer than the number of properties on system calls. In this study, we collect the Native APIs which are Windows kernel data generated by running program code. Then we adopt the normalized freqeuncy of Native APIs as the basic properties. In addition, the basic properties are transformed to new properties by GLDA(Generalized Linear Discriminant Analysis) that is an effective method to discriminate between malicious code and normal code, although the number of training data is fewer than the number of properties. To detect the malicious code, kNN(k-Nearest Neighbor) classification, one of the bayesian classification technique, was used in this paper. We compared the proposed detection method with the other methods on collected Native APIs to verify efficiency of proposed method. It is presented that proposed detection method has a lower false positive rate than other methods on the threshold value when detection rate is 100%.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.968-975
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• 2023

Purpose: We would like to confirm that the false positive rate of flames/smoke is high when detecting fires. Propose a method and dataset to recognize and classify fire situations to reduce the false detection rate. Method: Using the video as learning data, the characteristics of the fire situation were extracted and applied to the classification model. For evaluation, the model performance of Yolov8 and Slowfast were compared and analyzed using the fire dataset conducted by the National Information Society Agency (NIA). Result: YOLO's detection performance varies sensitively depending on the influence of the background, and it was unable to properly detect fires even when the fire scale was too large or too small. Since SlowFast learns the time axis of the video, we confirmed that detects fire excellently even in situations where the shape of an atypical object cannot be clearly inferred because the surrounding area is blurry or bright. Conclusion: It was confirmed that the fire detection rate was more appropriate when using a video-based artificial intelligence detection model rather than using image data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.300-304
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• 2015

A low bit rate speech coder based on the non-uniform sampling technique is proposed. The non-uniform sampling technique is based on the detection of inflection points (IP). A speech block is processed by the IP detector, and the detected IP pattern is compared with entries of the IP database. The address of the closest member of the database is transmitted with the energy of the speech block. In the receiver, the decoder reconstructs the speech block using the received address and the energy information of the block. As results, the coder shows fixed data rate contrary to the existing speech coders based on the non-uniform sampling. Through computer simulation, the usefulness of the proposed technique is shown. The SNR performance of the proposed method is approximately 5.27 dB with the data rate of 1.5 kbps.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.1
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• pp.51-57
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• 2010

This study was to analyze the difference in pilot error by using the Signal Detection Theory. The task was to detect the targeted aircraft(signal) which is different shape from many other aircraft(noise). From the two experiments, we differentiated the task difficulty followed by change in noise stimuli. Experiment 1 was to search the signal stimuli(fighter plane) while the noise stimuli(cargo plane) were increasing. The results from the Experiment 1 showed the tendency to decrease the hit rate by increasing the number of noise stimuli. However, the false alarm rate was not increased. The sensitivity(d') showed quite high. In Experiment 2, a disturbance stimulus(helicopter) was added to noise stimuli. The result was generally similar to those of Experiment 1. However, the hit rate was lower than that of Experiment 1.