• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.36 no.6
• /
• pp.547-555
• /
• 2023

High-density crossbar arrays based on storage class memory (SCM) are ideally suited to handle an exponential increase in data storage and processing as a central hardware unit in the era of AI-based technologies. To achieve this, selector devices are required to be co-integrated with SCM to address the sneak-path current issue that indispensably arises in such crossbar-type architecture. In this perspective, we first summarize the current state of tellurium-based threshold-switching devices and recent advances in the material, processing, and device aspects. We thoroughly review the physicochemical properties of elemental tellurium (Te) and representative binary tellurides, their tailored deposition techniques, and operating mechanisms when implemented in two-terminal threshold switching devices. Lastly, we discuss the promising research direction of Te-based selectors and possible issues that need to be considered in advance.

• The KIPS Transactions:PartC
• /
• v.10C no.4
• /
• pp.455-462
• /
• 2003

Techniques for the reduction of SRTS jitter and pointer adjustment jitter are studied. To reduce the stuffing jitter several methods have been proposed, such as bit leaking, stuff threshold modulation and sigma delta modulation. The characteristics of jitter generated in SRTS and pointer adjustment systen implementing these reduction techniques is analyzed with computer simulation. The results show that ms jitter value decreases to less than 50% as compared to a conventional pointer adjustment system. The amplitude of SRTS jitter using new techniques decreases or Increases dependent on system parameter.

• PNF and Movement
• /
• v.18 no.2
• /
• pp.245-254
• /
• 2020

Purpose: The study investigated the effects of extracorporeal shock wave therapy with myofascial release techniques (ESWT+MFR) on pain, movement, and function in patients with myofascial pain syndrome. Methods: Forty participants with upper trapezius trigger points were recruited and randomly allocated to two groups: an experimental group (n = 20) and a control group (n = 20). The experimental group performed the ESWT+MFR, and the control group performed only myofascial release techniques. Each group was treated for 15 minutes, twice a week for four weeks. Pain was assessed using a visual analogue scale and a pressure pain threshold measure. Movement was assessed by cervical range of motion, and cervical and shoulder function were assessed on the Constant-Murley Scale and the Neck Disability Index before and after treatment. Results: The results indicate statistically significant improvements in the two groups on all parameters after intervention as compared to baseline (p < 0.05). As compared to the control group, the experimental group showed statistically significant improvements on the visual analogue scale and pressure pain threshold, cervical range of motion (except rotation), and on the Neck Disability Index (p < 0.05). Conclusion: The ESWT+MFR is more effective than myofascial release techniques for pain, movement, and function in patients with myofascial pain syndrome and would be clinically useful for physical therapists treating myofascial pain syndrome.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.8 no.3
• /
• pp.721-726
• /
• 2004

The image binarization is applied frequently as one part of the preprocessing phase for a variety of image processing techniques such as character recognition and image analysis, etc. The performance of binarization algorithms is determined by the selection of threshold value for binarization, and most of the previous binarization algorithms analyze the intensity distribution of the original images by using the histogram and determine the threshold value using the mean value of Intensity or the intensity value corresponding to the valley of the histogram. The previous algorithms could not get the proper threshold value in the case that doesn't show the bimodal characteristic in the intensity histogram or for the case that tries to separate the feature area from the original image. So, this paper proposed the novel algorithm for image binarization, which, first, segments the intensity range of grayscale images to several intervals and calculates mean value of intensity for each interval, and next, repeats the interval integration until getting the final threshold value. The interval integration of two neighborhood intervals calculates the ratio of the distances between mean value and adjacent boundary value of two intervals and determine as the threshold value of the new integrated interval the intensity value that divides the distance between mean values of two intervals according to the ratio. The experiment for performance evaluation of the proposed binarization algorithm showed that the proposed algorithm generates the more effective threshold value than the previous algorithms.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.2
• /
• pp.225-233
• /
• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.34 no.6
• /
• pp.419-427
• /
• 2014

Nonlinear features of ultrasonic waves are more sensitive to the presence of a fatigue crack than their linear counterparts are. For this reason, the use of nonlinear ultrasonic techniques to detect a fatigue crack at its early stage has been widely investigated. Of the different proposed techniques, laser nonlinear wave modulation spectroscopy (LNWMS) is unique because a pulse laser is used to exert a single broadband input and a noncontact measurement can be performed. Broadband excitation causes a nonlinear source to exhibit modulation at multiple spectral peaks owing to interactions among various input frequency components. A feature called maximum sideband peak count difference (MSPCD), which is extracted from the spectral plot, measures the degree of crack-induced material nonlinearity. First, the ratios of spectral peaks whose amplitudes are above a moving threshold to the total number of peaks are computed for spectral signals obtained from the pristine and the current state of a target structure. Then, the difference of these ratios are computed as a function of the moving threshold. Finally, the MSPCD is defined as the maximum difference between these ratios. The basic premise is that the MSPCD will increase as the nonlinearity of the material increases. This technique has been used successfully for localizing fatigue cracks in metallic plates.

• Journal of Environmental Science International
• /
• v.30 no.12
• /
• pp.1101-1111
• /
• 2021

In this study, among the W-S-R(Wiper-Signal-Rainfall) relationship methods used to produce sensor-based rain information in real time, we sought to produce actual rainfall information by applying machine learning techniques to account for the effects of wiper operation. To this end, we used the gradient descent and threshold map methods for pre-processing the cumulative value of the difference before and after wiper operation by utilizing four sensitive channels for optical sensors which collected rain sensor data produced by five rain conditions in indoor artificial rainfall experiments. These methods produced rainfall information by calculating the average value of the threshold according to the rainfall conditions and channels, creating a threshold map corresponding to the 4 (channel) × 5 (considering rainfall information) grid and applying Optima Rainfall Intensity among the big data processing techniques. To verify these proposed results, the application was evaluated by comparing rainfall observations.

• Korean Journal of Remote Sensing
• /
• v.29 no.6
• /
• pp.645-655
• /
• 2013

As one of segmentation techniques for Synthetic Aperture Radar (SAR) image with oil spill, we applied a bimodal histogram method to discriminate oil pixels from non-oil pixels. The threshold of each moving window was objectively determined using the two peaks in the histogram distribution of backscattering coefficients from ENVISAT ASAR image. To reduce the effect of wind speed on oil spill detection, we selected ASAR image which satisfied a limit of wind speeds for successful detection. Overall, a commonly used adaptive threshold method has been applied with a subjectively-determined single threshold. In contrast, the bimodal histogram method utilized herein produces a variety of thresholds objectively for each moving window by considering the characteristics of statistical distribution of backscattering coefficients. Comparison between the two methods revealed that the bimodal histogram method exhibited no significant difference in terms of performance when compared to the adaptive threshold method, except for around the edges of dark oil spots. Thus, we anticipate that the objective method based on the bimodality of oil slicks may also be applicable to the detection of oil spills from other SAR imagery.

• KIISE Transactions on Computing Practices
• /
• v.23 no.3
• /
• pp.188-193
• /
• 2017

In this paper, we propose automatic OpenCL optimization techniques that offer the best performance for image processing programs on any hardware system. Developers should seek a proper way of parallelization and an appropriate work-group size for the architecture of target compute devices to achieve the best performance. However, testing potential devices to find them is both time-consuming and costly. Our techniques automatically set up hardware-optimized parallelization and find a suitable work-group size for the target device. Furthermore, using OpenCL does not always provide better performance in image processing. Hence, we also propose a way to automatically search for a threshold image size to allow image processing programs to decide whether or not to use OpenCL. Our findings demonstrate that out techniques improve the image processing performance significantly.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.11 no.5
• /
• pp.58-70
• /
• 2008

Earlier researches on Sensor Networks preferred symmetric key-based authentication schemes in consideration of limitations in network resources. However, recent advancements in cryptographic algorithms and sensor-node manufacturing techniques have opened suggestion to public key-based solutions such as Merkle tree-based schemes. These previous schemes, however, must perform the authentication process one-by-one in hierarchical manner and thus are not fit to be used as primary authentication methods in sensor networks which require mass of multiple authentications at any given time. This paper proposes a new concept of public key-based authentication that can be effectively applied to sensor networks. This scheme is based on exponential distributed data concept, a derivative from Shamir's (t, n) threshold scheme, in which the authentication of neighbouring nodes are done simultaneously while minimising resources of sensor nodes and providing network scalability. The performance advantages of this scheme on memory usage, communication overload and scalability compared to Merkle tree-based authentication are clearly demonstrated using performance analysis.