• Applied Chemistry for Engineering
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• v.22 no.3
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• pp.301-307
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• 2011

Optimal culture conditions of Spirulina platensis NIES 39 have been established using different types of light sources. Several types of photobioreactors were designed and the increase of biomass, the amount of $CO_2$, fixation and the production of chlorophyll content were studied. The result revealed that the input conditions of a 10 min period per 4 h at the condition of 5% $CO_2$ and 0.1 vvm, were excellent in the growth. The growth showing the maximum biomass accumulation is limited to 1.411 g/L when using the fluorescent bulb and the low powered surface mount device (SMD) type LEDs which were equipped-inside in the photobioreactor. However, the biomass exceeded up to 1.758 g/L level when a high powered red LED (color temperature : 12000 K) photobioreactor system was used. The $CO_2$ fixation speed and rate were increased. Although the total production of chlorophyll content undergoes a proportional increase in the biomass, the net content per dry cell weight (DCW) showed the higher production with a blue LED (color temperature : 7500 K) light than that of any other wavelengths. The carbon dioxide loss was marked as 0.15% of the inlet gas (5% $CO_2/Air$, v/v) at the maximum biomass culture condition.

• Korean Journal of Applied Biomechanics
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• v.31 no.1
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• pp.72-78
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• 2021

Objective: The purpose of this study was to investigate relations and effectiveness about mountain climbling exercise with different level of support surfaces by analyzing heart rate and EMG data. A total of 10 male college students with no musculoskeltal disorder were recruited for this study. Method: The biomechanical analysis was performed using heart rate monitor (Polar V800, Polar Electro Oy, Finland), step-box, exercise mat, and EMG device (QEMG8, Laxtha Inc. Korea, sampling frequency = 1,024 Hz, gain = 1,000, input impedance > 1012 Ω, CMRR > 100 dB). In this research, step-box were used to create different surface levels on the upper body (flat surface, 10% of subject's height, 20% of subject's height, and 30% of subject's hight). Based on these different conditions, data was collected by performing mountain climbing exercise during 30 seconds. Subjects were given 5 minutes of break to prevent muscular fatigue after each exercise. For each dependent variable, a one-way analysis of variance with repeated measures was conducted to find significant differences and Bonferroni post-hoc test was performed. Results: The results of this study showed that exercise intensity was reduced statistically as increased surface level on the upper body. Muscle activity of the upper rectus abdominis and biceps femoris for 30% of surface level was significantly higher than the corresponding values for flat surface. However, the opposite was found in the rectus femoris. In general, muscle activity of the lower rectus abdominis, erector spinae, external oblique abdominis, and gluteus maximus increased when surface level increased, but the differences were not significant. Conclusion: As a result, the increase in surface level of the body would change muscle activity of the upper body, indicating that different surface level of the upper body may cause significant effect on particular muscles to be more active during mountain climbing exercise. Based on results of this study, it is suggested to set up an appropriate surface level to target particular muscle to expect an effective training. It is also important to set adequate surface levels to create an effective training condition for preventing exercise injuries.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.45-54
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• 2022

This paper is about designing and implementing the simulation training equipment (simulator) for the CBRN Reconnaissance Vehicle-II (armor type). The simulation training equipment (simulator) is a military training equipment in a virtual environment that analyzes the training using various CBRN equipment according to the CBRN situation and make a professional report. The controller or training instructor can construct a scenario using the instructor control system for a possible CBRN situation, spread the situation, and observe the process of the trainee performing the propagated situation appropriately. All process can be monitored and analyzed by the system, and it can be recorded, so it is also used for AAR (After Action Review). To implement CBRN situation training in a virtual environment, instructor control (IOS), host (HOS), video (IGS), input/output device (IOC), and sound (ACS) were implemented, a long-range chemical automatic detector (LCA), a combined chemical detector (CAD), a control (MCC) and an operation (OCC) computer were developed as simulators. In this paper, the design and development of simulation training equipment for CBRN Reconnaissance Vehicle-II (armor type) was conducted, and the performance was verified through integrated tests and acceptance tests.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.11-19
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• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of IKEEE
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• v.27 no.1
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• pp.45-52
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• 2023

Cold-junction correction(CJC) and disconnection detection circuit design of various thermocouples(TC) and multi-channel TC interface circuit using them were designed. The CJC and disconnection detection circuit consists of a CJC semiconductor device, an instrumentation amplifier(IA), two resistors and a diode for disconnection detection. Based on the basic circuit, a multi-channel interface circuit was also implemented. The CJC was implemented using compensation semiconductor and IA, and disconnection detection was detected by using two resistor and a diode so that IA input voltage became -0.42V. As a result of the experiment using R-type TC, the error of the designed circuit was reduced from 0.14mV to 3㎶ after CJC in the temperature range of 0℃ to 1400℃. In addition, it was confirmed that the output voltage of IA was saturated from 88mV to -14.2V when TC was disconnected from normal. The output voltage of the designed circuit was 0V to 10V in the temperature range of 0℃ to 1400℃. The results of the 4-channel interface experiment using R-type TC were almost identical to the CJC and disconnection detection results for each channel. The implemented multi-channel interface has a feature that can be applied equally to E, J, K, T, R, and S-type TCs by changing the terminals of CJC semiconductor devices and adjusting the IA gain.

• Design Convergence Study
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• v.15 no.1
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• pp.135-150
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• 2016

Due to the development of communication technology and expansion of Intelligent Transport System (ITS), the car is changing from a simple mechanical device to second living space which has comprehensive convenience function and is evolved into the platform which is playing as an interface for this role. As the interface area to provide various information to the passenger is being expanded, the research importance about smart car based user experience is rising. This study has a research objective to propose the guidelines regarding the smart car user experience elements. In order to conduct this study, smart car user experience elements were defined as function, interaction, and surface and through the discussions of UX/UI experts, 8 representative techniques, 14 representative techniques, and 8 locations of the glass windows were specified for each element. Following, the smart car users' priorities of the experience elements, which were defined through targeting 100 drivers, were analyzed in the form of questionnaire survey. The analysis showed that the users' priorities in applying the main techniques were in the order of safety, distance, and sensibility. The priorities of the production method were in the order of voice recognition, touch, gesture, physical button, and eye tracking. Furthermore, regarding the glass window locations, users prioritized the front of the driver's seat to the back. According to the demographic analysis on gender, there were no significant differences except for two functions. Therefore this showed that the guidelines of male and female can be commonly applied. Through user requirement analysis about individual elements, this study provides the guides about the requirement in each element to be applied to commercialized product with priority.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.112-118
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• 2009

This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

• Korean Journal of Agricultural Science
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• v.51 no.3
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• pp.315-329
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• 2024

This study was conducted for simulation model development of an electric all-wheel-drive vehicle to adapt the agricultural machinery. Data measurement system was installed on a four-wheel electric driven vehicle using proximity sensor, torque-meter, global positioning system (GPS) and data acquisition (DAQ) device. Axle torque and rotational speed were measured using a torque-meter and a proximity sensor. Driving test was performed on an upland field at a speed of 7 km·h^-1. Simulation model was developed using a multi-body dynamics software, and tire properties were measured and calculated to reflect the similar road conditions. Measured and simulated data were compared to validate the developed simulation model performance, and axle rotational speed was selected as simulation input data and axle torque and power were selected as simulation output data. As a result of driving performance, an average axle rotational speed was 115 rpm for each wheel. Average axle torque and power were 4.50, 4.21, 4.04, and 3.22 Nm and 53.42, 50.56, 47.34, and 38.07 W on front left, front right, rear left, and rear right wheel, respectively. As a result of simulation driving, average axle torque and power were 4.51, 3.9, 4.16, and 3.32 Nm and 55.79, 48.11, 51.62, and 41.2 W on front left, front right, rear left, and rear right wheel, respectively. Absolute error of axle torque was calculated as 0.22, 7.36, 2.97, and 3.11% on front left, front right, rear left, rear right wheel, respectively, and absolute error of axle power was calculated as 4.44, 4.85, 9.04, and 8.22% on front left, front right, rear left, and rear right wheel, respectively. As a result of absolute error, it was shown that developed simulation model can be used for driving performance prediction of electric driven vehicle. Only straight driving was considered in this study, and various road and driving conditions would be considered in future study.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.