• ETRI Journal
• /
• v.37 no.3
• /
• pp.551-561
• /
• 2015

We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 1999.11a
• /
• pp.286-289
• /
• 1999

In order to detect the partial discharge with the metallic particle in GIS, AE sensor was designed and simulated by ANSYS, and manufactured as the coupled vibration mode. The resonant frequency of three Coupled AE sensors were as follows ; 147.88 kHz in 8.1 mm$\Phi$ $\times$ 8.1mm, 128.82 kHz in 9.5 mm$\Phi$ $\times$ 9.5mm, 85.22 kHz in 14.3mm$\Phi$ $\times$14.3mm. That frequency is λ/2 resonant frequency. AE sensor of 9.5mm$\Phi$ $\times$9.5mm responded higher than the other coupled vibration mode AE sensor at the partial discharge detection in GIS.

• Journal of the Korean Chemical Society
• /
• v.55 no.2
• /
• pp.204-207
• /
• 2011

A particle counting system that can also provide sensitive, specific chemical information, while consuming very less power, occupying less space, and being inexpensive has been developed. This system uses a microwave plasma torch (MPT) as the excitation source for atomic emission spectrometry (AES). Emission from a single particle can be detected, and the wavelength at which the emission is observed indicates the elements present in the particle. It is believed that correlating the particle size and emission intensity will allow us to estimate the particle size in addition to abovementioned capabilities of the system. In the long term, this system can be made field-portable, so that it can be used in atmospheric aerosol monitoring applications, which require real-time detection and characterization of particles at low concentrations.

• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.1680-1681
• /
• 2011

In this paper, particle counting system using a CMOS image sensor is demonstrated. The system utilizes a linear photodetector array as a detection element. Therefore, the particles are detected by large detection region, in contrast to a single detector in conventional particle counting devices, while maintaining the sensitivity. The advantage of proposed system is that particles are detected in a relatively large area without using the particle focusing method. Also, proposed system can be easily integrated with a microfluidic chip by attaching the device underneath the bottom plate of the microfluidic chip. Detection of polystyrene microbeads has been tested at a flow rate of 4.89mm/s. For 21 measurements, proposed system showed an average count error of 7.29% and a standard deviation of 4.74%. Potentially, the proposed system can detect even smaller particles simply by utilizing a higher resolution CMOS image sensor.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.9
• /
• pp.139-147
• /
• 2014

In this paper, we present a novel method for simultaneous detection and tracking of multiple objects using dual-layer particle filtering. The proposed dual-layer particle sampling (DLPS) algorithm consists of parent-particles (PP) in the first layer for detecting multiple objects and child-particles (CP) in the second layer for tracking objects. In the first layer, PPs detect persons using a classifier trained by the intersection kernel support vector machine (IKSVM) at each particle under a randomly selected scale. If a certain PP detects a person, it generates CPs, and makes an object model in the detected object region for tracking the detected object. While PPs that have detected objects generate CPs for tracking, the rest of PPs still move for detecting objects. Experimental results show that the proposed method can automatically detect and track multiple objects, and efficiently reduce the processing time using the sampled particles based on motion distribution in video sequences.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2011.06a
• /
• pp.855-856
• /
• 2011

• Journal of Information Processing Systems
• /
• v.18 no.1
• /
• pp.146-158
• /
• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.7
• /
• pp.3076-3092
• /
• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.9 no.1
• /
• pp.41-48
• /
• 2011

A mobile laser-induced breakdown detection (LIBD) system was developed for the field measurement of the size and concentration of aquatic colloidal nanoparticles sampled from Korea Atomic Energy Research Institute Underground Research Tunnel (KURT). The established LIBD apparatus is based on the optical detection of a laser-induced plasma by means of a two-dimensional optical imaging method for determining the size of nanoparticle. Calibration curve for determining the size of nanoparticle was obtained from the polystyrene reference particles of a well-defined size. The first direct application was made at KURT for investigating the particle sizes in groundwater. By comparing the size of particles in groundwater with the sizes of reference particles, the mean particle size of approximately $108{\pm}26$ nm with the concentration lower than 50 ppb was determined.

• Journal of Power Electronics
• /
• v.16 no.3
• /
• pp.1097-1109
• /
• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.