• Investigative Magnetic Resonance Imaging
• /
• v.18 no.4
• /
• pp.290-302
• /
• 2014

Purpose : Susceptibility-weighted magnetic resonance (MR) sequence is three-dimensional (3D), spoiled gradient-echo pulse sequences that provide a high sensitivity for the detection of blood degradation products, calcifications, and iron deposits. This pictorial review is aimed at illustrating and discussing its main clinical applications. Materials and Methods: SWI is based on high-resolution, 3D, fully velocity-compensated gradient-echo sequences using both magnitude and phase images. To enhance the visibility of the venous structures, the magnitude images are multiplied with a phase mask generated from the filtered phase data, which are displayed at best after post-processing of the 3D dataset with the minimal intensity projection algorithm. A total of 200 patients underwent MR examinations that included SWI on a 3 tesla MR imager were enrolled. Results: SWI is very useful in detecting multiple brain disorders. Among the 200 patients, 80 showed developmental venous anomaly, 22 showed cavernous malformation, 12 showed calcifications in various conditions, 21 showed cerebrovascular accident with susceptibility vessel sign or microbleeds, 52 showed brain tumors, 2 showed diffuse axonal injury, 3 showed arteriovenous malformation, 5 showed dural arteriovenous fistula, 1 showed moyamoya disease, and 2 showed Parkinson's disease. Conclusion: SWI is useful in detecting occult low flow vascular lesions, calcification and microbleed and characterising diverse brain disorders.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.2
• /
• pp.97-102
• /
• 2020

This paper relates with the Hybrid-SE-MMA (Signed-Error MMA) that is possible to improving the equalization performance by using the adaptive modulus and adaptive step size in SE-MMA adaptive equalizer for the minimizing the intersymbol interference. The equalizer tap coefficient is updatted use the error signal in MMA algorithm for adaptive equalizer. But the sign of error signal is used for the simplification of arithmetic operation in SE-MMA algorithm in order to updating the coefficient. By this simplification, we get the fast convergence speed and the reduce the algorithm processing speed, but not in the equalization performance. In this paper, it is possible to improve the equalization performance by computer simulation applying the adaptive modulus to the SE-MMA which is proposional to the power of equalizer output signal. In order to compare the improved equalization performance compared to the present SE-MMA, the recovered signal constellation that is the output of the equalizer, residual isi, MD(maximum distortion), MSE and the SER perfomance that means the robustness to the external noise were used. As a result of computer simulation, the Hybrid-SE-MMA improve equalization performance in the residual isi and MD, MSE, SER than the SE-MMA.

• Magazine of the Korea Concrete Institute
• /
• v.5 no.4
• /
• pp.167-178
• /
• 1993

The objective of this paper is to look into the possibility of the detailed and practical optimum design of rt:inforced concrete beam using methods oi discrete mathematical programming. In this discrete optimum formulation, the design variables are the overall depth, width and effective depth of members, and area of longitudinal reinforcement. In addition, the details such as the amount of web reinforcement and cutoff points of longitudinal reinforcement are also considered as variables. Total cost has been used as the objective function. The constraints include the code requirments such as flexural strength, shear strength, ductility, serviceability, concrete cover. spacing, web reinforcement, and development length and cutoff points of longitudinal renforcement. An optimization algorithm is presented for effective optimum design of R.C. beam with discrete de sign variables. First, the continuous variable optimization can be achieved by Feasible Direction Method. Using the results obtained from the continuous variable optimization, a branch and bound method is used to obtained the discrete design values. The proposed algorithm is applied to test problem for reliability, and the results are compared with those of graphical method and rounded-up method. And a simply supported R.C. beam and a two-span continuous R.C. beam are presented as numerical examples for effectiveness and applicability. It is considered that the presented algorithm can be effectively applied to the discrete optimum design of R.C. beams.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.2
• /
• pp.31-41
• /
• 2024

Deep learning models show excellent performance in tasks such as image classification and object detection in the field of computer vision, and are used in various ways in actual industrial sites. Recently, research on improving robustness has been actively conducted, along with pointing out that this deep learning model is vulnerable to hostile examples. A hostile example is an image in which small noise is added to induce misclassification, and can pose a significant threat when applying a deep learning model to a real environment. In this paper, we tried to confirm the robustness of the edge-learning classification model and the performance of the adversarial example detection model using it for adversarial examples of various algorithms. As a result of robustness experiments, the basic classification model showed about 17% accuracy for the FGSM algorithm, while the edge-learning models maintained accuracy in the 60-70% range, and the basic classification model showed accuracy in the 0-1% range for the PGD/DeepFool/CW algorithm, while the edge-learning models maintained accuracy in 80-90%. As a result of the adversarial example detection experiment, a high detection rate of 91-95% was confirmed for all algorithms of FGSM/PGD/DeepFool/CW. By presenting the possibility of defending against various hostile algorithms through this study, it is expected to improve the safety and reliability of deep learning models in various industries using computer vision.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.19 no.2
• /
• pp.92-98
• /
• 2015

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring (SHM) technique is increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and influenced by various external loads. "Abnormal behavior point" is a moment when the structure starts vibrating abnormally and this can be detected by comparing between before and after abnormal behavior point. In other words, anomalous behavior is a sign of damage on structures and estimating the abnormal behavior point can be directly related to the safety of structure. Abnormal behavior causes damage on structures and this leads to enormous economic damage as well as damage for humans. This study proposes an estimating technique to find abnormal behavior point using Hilber-Huang Transform which is a time-frequency signal analysis technique and the proposed algorithm has been examined through laboratory tests with a bridge model using a shaking table.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.2
• /
• pp.164-170
• /
• 2002

Sliding mode control, as a typical method of variable structure control, has the robust characteristics for the uncertainty and the disturbance of the nonlinear system. Because, however, sliding mode control input includes a sign function that Is discontinuous on the predefined switching surface, its applications are primarily limited by the need of alleviation or reduction of chattering. In this paper, we propose a chattering alleviation strategy based on a special nonlinear function and a fuzzy system. By using the proposed control scheme, we can reduce the steady state error. Its tracking performance is as fast as that of conventional method using the fixed boundary layer. Especially, in the proposed method, we can adjust the trade-off between the steady state error and the degree of chattering by regulating the proper range of the output variable of the fuzzy system. To verify the validity of the proposed algorithm, the analysis of the control method using the fixed boundary layer and the computer simulations are shown to compare with them.

• Journal of KIISE
• /
• v.42 no.10
• /
• pp.1231-1238
• /
• 2015

Traffic light rules are one among the most common and important safety rules as the directly correlate with the safety of pedestrians. Consequently, an algorithm is required to cause an automated (or semi-automated) vehicle to observe traffic light signals. We present a novel, artificial potential function to guide an automated vehicle through traffic lights. Our function consists of three potential function components representing the three traffic light colors: green, yellow, and red. The traffic light potential function smoothly changes an artificial potential field using the elapsed time for the current light and light conversion. Our traffic light potential function is combined with other potential functions to guide vehicles' movement and constructs the final artificial potential field. Using various simulations, we found or method successfully guided the vehicle to observe traffic lights while behaving like human-controlled cars.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.23 no.11
• /
• pp.1434-1443
• /
• 1999

The turbulent fluctuations of temperature and two components of velocity have been measured with hot- and cold-wires in the Thermally Stratified Wind Tunnel(TSWT). Using the fin-tube heat exchanger type heaters and the neural network control algorithm, both stable ($dT/dz=109.4^{\circ}C$) and unstable ($dT/dz=-49.1^{\circ}C$) stratifications were realized. An ambient air jet was issued normally into the cross flow($U_{\infty}=1.0 m/s$) from a round nozzle(d = 6 mm) flushed at the bottom waII of the wind tunnel with the velocity ratio of $5.8(U_{jet}/U_{\infty})$. The characteristics of turbulent dispersion in the cross flow jet are found to change drastically depending on the thermal stratification. Especially, in the unstable condition, the vertical velocity fluctuation increases very rapidly at downstream of jet. The fluctuation velocity spectra and velocity-temperature cospectra along the jet centerline were obtained and compared. In the case of stable stratification, the heat flux cospectra changes Its sign from a certain point at the far field because of the restratification phenomenon. It is inferred that the main reason in the difference between the vertical heat fluxes is caused by the different length scales of the large eddy motions. The turbulent kinetic energy and scalar dissipation rates were estimated using partially non-isotropic and isotropic turbulent approximation. In the unstable case, the turbulent energy dissipation decreases more rapidly with the downstream distance than in the stable case.

• The KIPS Transactions:PartC
• /
• v.11C no.6 s.95
• /
• pp.719-724
• /
• 2004

Recently, since the number of internet users is increasing rapidly and, by using the public hacking tools, general network users can intrude computer systems easily, the hacking problem is getting more serious. In order to prevent the intrusion, it is needed to detect the sign in advance of intrusion in a positive prevention by detecting the various foms of hackers' intrusion trials to know the vulnerability of systems. The existing network-based anomaly detection algorithms that cope with port- scanning and the network vulnerability scans have some weakness in intrusion detection. they can not detect slow scans and coordinated scans. therefore, the new concept of algorithm is needed to detect effectively the various forms of abnormal accesses for intrusion regardless of the intrusion methods. In this paper, SPAD(Session Pattern Anomaly Detector) is presented, which detects the abnormal service patterns by comparing them with the ordinary normal service patterns.

• Current Optics and Photonics
• /
• v.1 no.3
• /
• pp.196-202
• /
• 2017

Corona discharge is always a sign of failure processes of high-voltage electrical apparatus, including those utilized in electric railway systems. Solar-blind ultraviolet (UV) cameras are effective tools for corona inspection. In this work, we present an automatic railway corona-discharge detection system based on solar-blind ultraviolet detection. The UV camera, mounted on top of a train, inspects the electrical apparatus, including transmission lines and insulators, along the railway during fast cruising of the train. An algorithm based on the Hough transform is proposed for distinguishing the emitting objects (corona discharge) from the noise. The detection system can report the suspected corona discharge in real time during fast cruises. An experiment was carried out during a routine inspection of railway apparatus in Xinjiang Province, China. Several corona-discharge points were found along the railway. The false-alarm rate was controlled to less than one time per hour during this inspection.