• Nuclear Engineering and Technology
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• v.35 no.2
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• pp.165-170
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• 2003

We developed an improved 3-PM liquid scintillation counting (3-PM LSC) method in which three detectors can be displaced to back and forth directions, and a data acquisition system being able to provide the values for all parameters required for the method. The detectors are entirely located in a 20-mm lead chamber of an inner diameter of 500 mm. A saw-toothed gear ties up all detectors so as to move them uniformly, up to 50 mm with unit of 1 mm. The data acquisition system was designed in an integrated circuit to perform the necessary works such as fast amplification, discrimination, coincidence and logic analysis. It generates values of nine parameters among twelve's generated in the 3-PH LSC method. The dead time of each counting channel is of extending type, valving from 10 to 100 $mutextrm{s}$. We measured the TDCR values with an unquenched liquid scintillation source 1"C by displacing the detectors with a step of 2.5 mm away from counting vial. Their values were derived on the range from 0.9 to 0.6. The extent is three times wider than those regions observed by applying the defocalization technique.ique.

• Smart Media Journal
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• v.10 no.4
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• pp.15-20
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• 2021

The distributed trigger counting problem is to notify the user when the total number of triggers received from a distributed system consisting of n nodes reaches a predefined value w. The distributed trigger counting problem is used for monitoring and global snapshots in various distributed systems. In this paper, we propose a simple and efficient algorithm for the distributed trigger counting problem. The proposed algorithm operates based on a tree structure of degree $\sqrt{n}$ and height 2. The proposed algorithm operates in three different phases based on the remaining number of triggers. Experimental results show that the proposed algorithm has a smaller message complexity than CoinRand, and MaxRcv also performed better when the number of nodes is not large.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1361-1368
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• 2020

The system for counting the number of people has traditionally been implemented in various ways. Existing systems include infrared sensors, lasers, cameras, etc. In the case of such an existing system, there are restrictions on space such as ceilings and sides of walls. In this paper, we propose a method of detecting the footsteps of pedestrians using a piezoelectric mat containing a number of piezoelectric sensors and counting the number of pedestrians passing simultaneously by using the data collected from the piezoelectric mat. When pedestrians pass over piezoelectric mats, the collected sensor data was aggregated using SPI communication and transmitted to PC server using TCP/IP communication. Performance analysis shows that approximately 600 step data can be recognized with 99% accuracy. This is to overcome the shortcomings of other counting systems.

• Journal of the Optical Society of Korea
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• v.20 no.5
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• pp.579-585
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• 2016

Integral imaging combined with photon-counting detection has been researched for three-dimensional information sensing under low-light-level conditions. This paper addresses the extraction of distance information with photon-counting integral imaging. The longitudinal distance to the object is obtained utilizing photon-counting elemental images. The pixel disparity is estimated by maximizing the nonlinear correlation of photocounts. The first- and second-order statistical properties of the nonlinear correlation are theoretically derived. In the experiments, these properties are verified by varying the mean number of photocounts in the scene. The average distance is compared to that from the intensity information, showing the robustness of the proposed system even at low photocounts.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.103-107
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• 2007

In this paper, the light screen sensing system is introduced and testified to detect small grains such as seeds or electronic chips of uneven sized. Two modules composed of transmitter-receiver sensor array and microprocessor-based sensor signal processing system are developed to realize the proposed system. Experimental results showed that the sensing signal was relatively clear and its counting performance was very stable.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.547-552
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• 2015

In this paper, an ARS-EKF based motion counting algorithm for repetitive exercises such as calisthenics is proposed using a smartwatch. Raw sensor signals from accelerometers and gyroscopes are widely used for conventional smartwatch counting algorithms based on pattern recognition. However, generated features from raw data are not intuitive to reflect the movement of motions. The proposed motion counter algorithm is composed of navigation based feature generation and counting with error correction. The candidate features for each activity are velocity and attitude calculated through an ARS-EKF algorithm. In order to select those features which reveal the characteristics of each motion, an exercise frame from the initial sensor frame is introduced. Counting processes are basically based on the zero crossing method, and misdetected counts are eliminated via simple classification algorithms considering the frequency of the counted motions. Experimental results show that the proposed algorithm efficiently and accurately counts the number of exercises.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.39-46
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• 2015

This paper presents an automatic method to detect and count the cells from microorganism images based on mobile environments. Cell counting is an important process in the field of biological and pathological image analysis. In the past, cell counting is done manually, which is known as tedious and time consuming process. Moreover, the manual cell counting can lead inconsistent and imprecise results. Therefore, it is necessary to make an automatic method to detect and count cells from biological images to obtain accurate and consistent results. The proposed multi-step cell counting method automatically segments the cells from the image of cultivated microorganism and labels the cells by utilizing topological analysis of the segmented cells. To improve the accuracy of the cell counting, we adopt watershed algorithm in separating agglomerated cells from each other and morphological operation in enhancing the individual cell object from the image. The system is developed by considering the availability in mobile environments. Therefore, the cell images can be obtained by a mobile phone and the processed statistical data of microorganism can be delivered by mobile devices in ubiquitous smart space. From the experiments, by comparing the results between manual and the proposed automatic cell counting we can prove the efficiency of the developed system.

• Journal of the Society of Naval Architects of Korea
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• v.52 no.3
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• pp.222-235
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• 2015

The purpose of this study is to suggest guidelines for riser fatigue analysis in terms of selection of reasonable analysis method. Three analysis methods (spectral, regular wave, rain-flow counting) are introduced and compared. As the riser systems give non-linear response, the time-domain analysis method is more preferred than frequency-domain analysis method. The spectral fatigue analysis method, however, is still useful for identifying fatigue prone areas. Once stress RAO is established, fatigue damage can be calculated very quickly. The regular wave method and the rain-flow counting method are more time consuming but give more exact results compare to spectral method. In case of regular wave method, a set of regular waves which represent random sea states is considered for dynamic analysis. The rain-flow counting method is the most intuitive and exact method because it refers time history stresses containing most of non-linear effects of the riser system. However, it is not common for early design stage to use rain-flow counting method because of its high cost. In this study, it was confirmed that the regular wave method is the most cost effective way in specific cases. However, if the system is highly non-linear, it seems that the regular wave method gives less accurate results than rain-flow counting method. Therefore, it is imperative that the engineers select appropriate analysis method based on design stage and given engineering period. This paper also discusses the theoretical background of each calculation method and hydrodynamic aspects of marine riser systems. A steel catenary riser (SCR) line on FPSO was considered and marine dynamic program (OrcaFlex) was used for static and dynamic analysis.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1680-1681
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• 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 Information Processing Systems
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• v.18 no.3
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• pp.428-442
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• 2022

A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficient scheduling and management of transport routes. In this study, we introduce a lightweight head detection network using deep learning applicable to an embedded system. Currently, object detection algorithms using deep learning have been found to be successful. However, these algorithms essentially need a graphics processing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network using certain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using the proposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK 710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiency of the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system, we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances of exiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite the fact that only the CPU was used.