• Journal of IKEEE
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• v.24 no.3
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• pp.754-765
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• 2020

Currently, Intelligent femoral prostheses that support the corresponding mode in walking and specific movements are being studied. Certain controls such as upstairs, sitting, and standing require a technique to classify control commands based on the user's intention because the mode must be changed before the operation. Therefore, in this paper, we propose a technique that can classify various control commands based on the user's intention in the intelligent thigh prosthesis system. If it is determined that the EMG signal needs to be compensated, the proposed technique compensates the EMG signal using the correlation between the strength and frequency components of the normal EMG signal and the muscle volume estimated by the pressure sensor. Through the experiment, it was confirmed that the user's intention was accurately detected even in the situation where muscle fatigue was accumulated. Improved intention detection techniques allow five control modes to be distinguished based on the number of muscle contractions within a given period of time. The results of the experiment confirmed that 97.5% accuracy was achieved through muscle tone compensation even if the strength of the muscle signal was different from normal due to muscle fatigue after exercise.

• 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 Intelligence and Information Systems
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• v.29 no.2
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• pp.23-34
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• 2023

Wearable technology for military applications has received considerable attention as a means of personal status check and monitoring. Among many, an implementation to recognize specific motion states of a human is promising in that allows active management of troops by immediately collecting the operational status and movement status of individual soldiers. In this study, as an extension of military wearable application research, a new ankle wearable device is proposed that can glean the information of a soldier on the battlefield on which action he/she takes in which environment. Presuming a virtual situation, the soldier's upper limbs are easily exposed to uncertainties about circumstances. Therefore, a sensing module is attached to the ankle of the soldier that may always interact with the ground. The obtained data comprises 3-axis accelerations and 3-axis rotational velocities, which cannot be interpreted by hand-made algorithms. In this study, to discern the behavioral characteristics of a human using these dynamic data, a data-driven model is introduced; four features extracted from sliced data (minimum, maximum, mean, and standard deviation) are utilized as an input of the model to learn and classify eight primary military movements (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). As a result, the proposed device could recognize a movement status of a solider with 95.16% accuracy in an arbitrary test situation. This research is meaningful since an effective way of motion recognition has been introduced that can be furtherly extended to various military applications by incorporating wearable technology and artificial intelligence.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.31-37
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• 2024

In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.19-28
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• 2004

This paper shows how to compensate the skew from the traffic sign included in the natural image and extract the text. The research deals with the Process related to the array image. Ail the process comprises four steps. In the first fart we Perform the preprocessing and Canny edge extraction for the edge in the natural image. In the second pan we perform preprocessing and postprocessing for Hough Transform in order to extract the skewed angle. In the third part we remove the noise images and the complex lines, and then extract the candidate region using the features of the text. In the last part after performing the local binarization in the extracted candidate region, we demonstrate the text extraction by using the differences of the features which appeared between the tett and the non-text in order to select the unnecessary non-text. After carrying out an experiment with the natural image of 100 Pieces that includes the traffic sign. The research indicates a 82.54 percent extraction of the text and a 79.69 percent accuracy of the extraction, and this improved more accurate text extraction in comparison with the existing works such as the method using RLS(Run Length Smoothing) or Fourier Transform. Also this research shows a 94.5 percent extraction in respect of the extraction on the skewed angle. That improved a 26 percent, compared with the way used only Hough Transform. The research is applied to giving the information of the location regarding the walking aid system for the blind or the operation of a driverless vehicle

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.97-107
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• 2013

On the Staggered Crosswalks, pedestrians cross the crosswalks two times. This method can reduce the cycle, the vehicle delay and the walking distance by increasing the major direction of green time. The safety of pedestrians is also effective. This study suggests the warrant of the facilities of island width and length etc. by considering the road structure and pedestrians. Also this study suggests the standard of the safety through the accident analysis of Staggered crosswalks and General Crosswalks. In the results, accident rate of the Staggered Crosswalks 18.3(100 million vehicle-km) was lower than the accident rate of the General Crosswalks 28.3(100million vehicle-km). By understanding the start point of crossing of the Staggered Crosswalks, the analysis of the location and types of accident suggests the safety zone(spare space). The setting warrants of Staggered Crosswalks are 4 lane over the road and the 2 meter over sidewalk width of island. The minimum length of the Pedestrian island was doubled compared to the crosswalks width. And the maximum length was set by considering the wait time of the pedestrians.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.1-11
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• 2016

The purpose of this study is to analyze the effect of public transit accessibility on the Carsharing use demand. By utilizing the rental historical DB of Greencar which is operated in Suwon city and public transit GIS DB, the use demand models for Carsharing by rental offices are built and analyzed in accordance with public transit accessibility. The result indicates 73% of walking as a majority, 3% cycling, and 20% using buses and urban railways to access Carsharing rental offices. The goodness of fit of Carsharing use models reflecting accessibility to buses and railways is verified as 0.818 which proves that public transit accessibility is a significant variable. Therefore, it is verified that installing Carsharing rental offices where public transit transfer is convenient can possibly increase the use demand. Especially, while accessibility to buses is verified as a significant variable out of other public transit means, the accessibility to urban railways is verified as not significant. This suggests that a variety of complementary policies such as transfer discount policy and one-way transfer return policy are necessary in between urban railways and Carsharing in order to promote mutual use demand in accordance with the other public transit means. This study result is yet the basic research on Carsharing, however it is expected to contribute to improvement of transfer demand in between different public transit means.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.100-115
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• 2020

Recently, with the development of various sensors and communication technologies, the market for wearable devices capable of recording physical activity in connection with a smartphone is expanding. The purpose of this study is to analyze physical activity for each transportation modes in order to utilize wearable devices in the field of transportation. This study consists of three steps: data collection, basic statistical analysis, and physical activity analysis. Four adult males and females were recruited as investigators, and physical activity and route information were collected through Fitbit, a commercial wearable device. From the collected physical activity information, a percentage of heart rate reserve (%HRR) using a heart rate was derived and used for analysis. As a results, it was found that there is a statistically significant difference in heart rate for each transportation mode, and physical activity intensity is the highest when walking. In addition, the results of physical activity analysis for the case of using different routes for the same OD were presented. The results presented in this study are expected to be used as basic data for preparing public transportation activation policies and providing customized services for the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.5
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• pp.13-21
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• 2013

Super high-rise buildings of combined use such as large shopping malls and multiplex etc. have larger parking facilities than general buildings and are characteristic of an increase in the number of the entrance and the exit connecting internal external space of the parking lot. These features cause a congestion of internal traffic by increasing car driving distance in the parking lot, and vehicle idling increases by drivers wander the parking lot in order to find parking space. In addition, they make drivers suffer from lots of difficulties due to parking including increasing their walking line after parking. Therefore, in this study, we developed individual parking information provision algorithm to specify the optimal parking place for drivers according to the purpose of visiting a building and the drivers' moving path, and selected new construction site for the second lotte world in order to evaluate the algorithm developed and performed evaluation. As a result of the evaluation, it was analyzed that in the case of applying the individual parking information provision algorithm compared to the existing parking information provision algorithm, moving distance in the parking lot decreases around 7.43~83.4%, and that in the case of $CO_2$ emission, it decreased about 47.7% on average, which indicates that the efficiency resulted from application of the individual parking information provision algorithm is very high as the application effects are tested.