• Journal of Ocean Engineering and Technology
• /
• v.9 no.2
• /
• pp.167-174
• /
• 1995

The anomlous signal, anomaly, recorded by a sub-bottem profiler is analized for exploration of buried objects in the ocean, This anomaly is known as a signal diffracted from the edge of the buried object. Signals obtained from model that and numerical simulation are analized for investigating characteristics of the diffracted signal. From this study a diffracted signal and a non-diffracted signal can be identified, and the location of the object can be obtained. In order to identify an object in the seafloor the dimension of the object should be greater than the wave length used for exploration, and the acoustic impedance should be much greater than that of sediments. A 2-trace stacking of the signals can enhance the feature of strongly diffracted signals whereas it can diminish weak signals.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.1
• /
• pp.169-189
• /
• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.10a
• /
• pp.619-622
• /
• 2004

For recent years, hacking is in the trends of making excessive traffic unnecessarily to obstruct the service by getting a system's performance down. And we can also see systems paralyzed in service ability due to the flash crowds of normal traffic to a popular website. This is an important problem certainly solved in the point of QoS guarantee for the clients. It is known that the former is DDoS(Distributed Denial of Service) attack and the latter is FE(Flash Event). These two are samely anomalous traffic because these make excessive congestion on the network or system and downgrade the system's service ability. In this paper, we propose a scheme for protecting the system against anomalous traffic and for guaranteeing the QoS. For this, a server records and maintains the information of clients accessed more than one time before when it is in normal condition. When it falls into the congestion, the records are used for filtering the spoofed IP. We send and receive the ICMP request/reply packet to know whether the IP is spoofed. And we also propose for applying the object spliting of CDN to guarantee the QoS in the initial FE situation.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.16 no.1
• /
• pp.27-35
• /
• 2016

Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.

• Journal of Electrical Engineering and Technology
• /
• v.6 no.6
• /
• pp.806-816
• /
• 2011

Interest about social security has recently increased in favor of safety for infrastructure. In addition, advances in computer vision and pattern recognition research are leading to video-based surveillance systems with improved scene analysis capabilities. However, such video surveillance systems, which are controlled by human operators, cannot actively cope with dynamic and anomalous events, such as having an invader in the corporate, commercial, or public sectors. For this reason, intelligent surveillance systems are increasingly needed to provide active social security services. In this study, we propose a core technique for intelligent surveillance system that is based on swarm robot technology. We present techniques for invader enclosing using swarm robots based on multiple distributed object environment. The proposed methods are composed of three main stages: location estimation of the object, specified object tracking, and decision of the cooperative behavior of the swarm robots. By using particle filter, object tracking and location estimation procedures are performed and a specified enclosing point for the swarm robots is located on the interactive positions in their coordinate system. Furthermore, the cooperative behaviors of the swarm robots are determined via the result of path navigation based on the combination of potential field and wall-following methods. The results of each stage are combined into the swarm robot-based invader-enclosing technique on multiple distributed object environments. Finally, several simulation results are provided to further discuss and verify the accuracy and effectiveness of the proposed techniques.

• Journal of The Korean Astronomical Society
• /
• v.35 no.2
• /
• pp.105-110
• /
• 2002

We present the results of VLA $NH_3$ (1,1) and (2,2) line observations of the young-stellar object (YSO) IRAS 19550+3248. The integrated intensity map of the $NH_3$ (1,1) line shows that there are two ammonia cores in this region; core A which is associated with the YSO, and core B which is diffuse and located at the northeast of core A. Core A is compact and elongated along the east-west direction (0.07 pc$\times$0.05 pc) roughly perpendicular to the molecular outflow axis. Core B is diffuse and extended (0.18 pc$\times$0.07 pc). $NH_3$ (2,2) line is detected only toward core A, which indicates that it is hotter (~ 15 K), presumably due to the heating by the YSO. The $NH_3$ (1,1) line toward core A is wide (${\Delta}v{\ge} 3 km s^{-l}$) and appears to have an anomalous intensity ratio of the inner satellite hyperfine lines. The large line width may be attributed to the embedded YSO, but the hyperfine anomaly is difficult to explain. We compare the results of $NH_3$ observations with those of previous CS observations and find that the CS emission is detected only toward core A and is much more extended than the $NH_3$ emission.

• Archives of Plastic Surgery
• /
• v.49 no.2
• /
• pp.240-252
• /
• 2022

Rather than just another "review," this is intended to be an "overview" of the entire subject of the medial sural artery perforator (MSAP) flap as has been presented in the reconstructive literature from its inception in 2001 until the present, with any exceptions not purposefully overlooked. Unfortunately, the pertinent anatomy of the MSAP flap is always anomalous like most other perforator flaps, and perhaps even more variable. No schematic exists to facilitate the identification of a dominant musculocutaneous perforator about which to design the flap, so some adjunctive technology may be highly valuable for this task. However, if a relatively thin free flap is desirable for a small or moderate sized defect that requires a long pedicle with larger caliber vessels, the MSAP flap deserves consideration. Indeed, for many, this has replaced the radial forearm flap such as for partial tongue reconstruction. Most consider the donor site deformity, even if only a conspicuous scar on the calf, to be a contraindication. Yet certainly if used as a local flap for the knee, popliteal fossa, or proximal leg, or as a free flap for the ipsilateral lower extremity where a significant recipient site deformity already exists, can anyone really object that this is not a legitimate indication? As with any perforator flap, advantages and disadvantages exist, which must be carefully perused before a decision to use the MSAP flap is made. Perhaps not a "workhorse" flap for general use throughout the body, the MSAP flap in general may often be a valuable alternative.

• Journal of the Korean Geophysical Society
• /
• v.3 no.3
• /
• pp.175-184
• /
• 2000

A nondestructive experimental feasibility study was conducted using magnetometer to find buried cultural objects at pottery and steel matters in low-relief mountaineous area of Doojeong-dong, Cheonan, Chungnam Province from May 23 to July 18, 1998. Magnetic survey was carried out with $20cm{\times}20cm$ grid in a site of $20m{\times}40m$ before excavation, and the distribution of magnetic anomalies was compared with the results of excavation. Magnetic sensor was located on the surface of ground during the magnetic survey on the basis of an experimental result. Positive magnetic anomalies of maximum 130 nT are found over a pair of potteries. Magnetic anomaly map reveals several anomalous points in the 1st and 4th quadrants of the survey site, from where potteries and their fragments were confirmed. Six points out of seven points cprrelated with magnetic anomaly are found contain earthwares, whereas a magnetically uncorrelated location produced earthware made of unbaked clay. Steel waste such as cans and wires hidden in soil and bushes also influenced magnetic anomalies. Therefore, it is better to remove such steel wastes prior to magnetic survey if possible. Some magnetically anomalous points produced no archaeological object on excavation. This may be explained by shallower level of excavation than burial depth.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.3
• /
• pp.171-179
• /
• 2020

Recently, studies have shown that convolution neural networks are achieving the best performance in image classification, object detection, and image generation. Vision based defect inspection which is more economical than other defect inspection, is a very important for a factory automation. Although supervised anomaly detection algorithm has far exceeded the performance of traditional machine learning based method, it is inefficient for real industrial field due to its tedious annotation work, In this paper, we propose ADGAN, a unsupervised anomaly detection architecture using the variational autoencoder and the generative adversarial network which give great results in image generation task, and demonstrate whether the proposed network architecture identifies anomalous images well on MNIST benchmark dataset as well as our own welding defect dataset.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.3
• /
• pp.279-292
• /
• 2022

Unexpected anomalies in the geotechnical investigation at design stage may cause problems during tunnel excavation. Therefore, it is important to predict the ground condition ahead of a tunnel face during tunnel excavation in order to prevent tunnel collapse. Despite the fulfillment of an electrical resistivity survey at the tunnel face, the existing electrical resistivity survey program can produce distorted results by the limitation of tunnel modelling. In this background, this study develops a modelling program for an electrical resistivity survey considering the tunnel shape. Numerical simulation and inverse calculation were performed for the electrical resistivity survey in the tunnel using the developed program. As a result, it was proved that the developed program could predict accurately the anomalous object's location and condition ahead of the tunnel face.