• Journal of the Korean Society for Precision Engineering
• /
• v.28 no.5
• /
• pp.606-613
• /
• 2011

In this paper, patterns of laser scattering and detection of micro defects have been investigated based on Rayleigh criterion for silicon wafer in solar cell. Also, a new laser scattering mechanism is designed using characteristics of light scattering against silicon wafer surfaces. Its parameters are to be optimally selected to obtain effective and featured patterns of laser scattering. The optimal parametric ranges of laser scattering are determined using the mean intensity of laser scattering. Scattering patterns of micro defects are investigated at the extracted parameter region. Among a lot of pattern features, both maximum connected area and number of connected component in patterns of laser scattering are regarded as the important information for detecting micro defects. Their usefulness is verified in the experiment.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.4
• /
• pp.353-359
• /
• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).

• Journal of Integrative Natural Science
• /
• v.3 no.1
• /
• pp.19-23
• /
• 2010

A simple and rapid method is described for detecting nitroaromatic explosives in air or seawater with the use of photoluminescent organosilicon compounds. The synthesis, spectroscopic characterization, and fluorescence quenching efficiency of silafluorenes are reported. Silafluorenes were synthesized from the reduction of dilithiobiphenyl with dichlorosilanes. Two silafluorenes were used for the detection of nitroaromatic compounds. Detection of nitroaromatic molecules, such as 2,4-dinitrotoluene (DNT), 2,4,6-trinitrotoluene (TNT), and picric acid (PA), has been explored. A linear Stern-Volmer relationship was observed for the first three analytes. Fluorescence spectra of silafluorenes obtained in either toluene solutions or thin films displayed no shift in the maximum of the emission wavelength. The photoluminescence quenching occurs by a static mechanism.

• International journal of advanced smart convergence
• /
• v.5 no.4
• /
• pp.54-56
• /
• 2016

Code-reuse attacks are very dangerous in various systems. This is because they do not inject malicious codes into target systems, but reuse the instruction sequences in executable files or libraries of target systems. Moreover, code-reuse attacks could be more harmful to IoT systems in the sense that it may not be easy to devise efficient and effective mechanism for code-reuse attack detection in resource-restricted IoT devices. In this paper, we propose a detection scheme with using Kullback-Leibler (KL) divergence to combat against code-reuse attacks in IoT. Specifically, we detect code-reuse attacks by calculating KL divergence between the probability distributions of the packets that generate from IoT devices and contain code region addresses in memory system and the probability distributions of the packets that come to IoT devices and contain code region addresses in memory system, checking if the computed KL divergence is abnormal.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.8 no.2 s.21
• /
• pp.14-21
• /
• 2005

This paper describes the system for detecting the tank crew's(loader's) hand, arm, head and the upper half of the body in a danger area between the turret ceiling and the upper breech mechanism by computer vision-based method. This system informs danger of pressed to death to gunner and commander for the safety of operating mission. The camera mounted ort the top portion of the turret ceiling. The system sets search moving object from this image and detects by using change of image, laplacian operator and clustering algorithm in this area. It alarms the tank crews when it's judged that dangerous situation for operating mission. The result In this experiment shows that the detection rate maintains in $81{\sim}98$ percents.

• 전기의세계
• /
• v.19 no.6
• /
• pp.18-25
• /
• 1970

The object of this project is to manifest the mechanism of deterioration phenomena for dielectrics causing corona discharge and applies it for determine the standard corona detection technique. As the results, we observed that corona discharges may occur more strongly around cylindrical shape electrode in air than hemisphere shape electrode in vacuum, so that it depends on effects such as shape of the electrode, moisture, surface coditions, etc. According to observed the deterioration of dielectrics takes place in following stages. At first the attacked surface by an electron avalanche is uniformly eroded; then pits are formed; after that sharp channels are formed which lead to break-down as a treeing. The test are accelerated with higher frequencies by the cylindrical bar shape electrode in the pulse stright detection method more sensitive than Lissajous patterns. Lissajous patterns detection method is simple but usually insensitive and has disadvantage that the magnitude of the individual discharge is not measured.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.05a
• /
• pp.562-565
• /
• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.107-112
• /
• 2022

The automotive industry is motivated to provide more and more amenities to its customers. The industry is taking advantage of artificial intelligence by increasing different sensors and gadgets in vehicles machoism is forward collision warning, at the same time road accidents are also increasing which is another concern to address. So there is an urgent need to provide an A.I based system to avoid such incidents which can be address by using artificial intelligence and global positioning system. Automotive/smart vehicles protection has become a major study of research for customers, government and also automotive industry engineers In this study a two layered novel hypothetical approach is proposed which include in-time vehicle/obstacle detection with auto warning mechanism for collision detection & avoidance and later in a case of an accident manifestation GPS & video camera based alerts system and interrupt generation to nearby ambulance or rescue-services units for in-time driver rescue.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.1-10
• /
• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

• The KIPS Transactions:PartB
• /
• v.11B no.3
• /
• pp.353-360
• /
• 2004

The study on scene change detection in the compressed MPEG videos has been done by various approaches. However, most of these approacher accomplished scene change detection by carrying out decoding processes and then by comparing pixels with pixels. This approach it not suitable for real time applications owing to much computing time of decoding processes. Recently, the study on scene change detection algorithms using only information of compressed domain is becoming Increasingly important. In this paper, we propose a sequential access method as an efficient scene change detection algorithm in the compressed domain. According to the type of pictures in the compressed MPEG video streams (divided in I-blocks and each I-block into P-blocks), the proposed algorithm provides effective scene change detection by applying sequential access and block by block mechanism. The proposed sequential access method provides fast and accurate detection operation by reducing checking procedures of unnecessary pictures due to molt of operations in compressed domain and checking by block units. Also, this approach uses optimal algorithm to provide fast and accurate detection operation.