• ETRI Journal
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• v.45 no.5
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• pp.862-873
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• 2023

Determining whether an autonomous self-driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three-camera model, which would enable us to more easily compile a variety of training data to endow our model with improved generalizability. In this work, we provide three separate fusion methods (feature, early, and late) of combining the information from three cameras. Extensive pedestrian-view intersection classification experiments show that our feature fusion model provides an area under the curve and F1-score of 82.00 and 46.48, respectively, which considerably outperforms contemporary three- and one-camera models.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.767-772
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• 2005

The Internet is a packet switched network which offers best-effort service, but current IP network provide enhanced services such Quality of Services, Virtual Private Network (VPN) services, Distribute Firewall and IP Security Gateways. All such services need packet classification for determining the flow. The problem is performing scalable packet classification at wire speeds even as rule databases increase in size. Therefore, this research offer packet classification algorithm that increase classifier performance when working with enlarge rules database by rearrange rule structure into Bitmap Intersection Lookup (BIL) tables. It will use packet's header field for looking up BIL tables and take the result with intersection operation by logical AND. This approach will use simple algorithm and rule structure, it make classifier have high search speed and fast updates.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.336-346
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• 2000

This paper presents a geometric method that can detect and compute all conic sections in the intersection of two tori. Conic sections contained in a torus must be circles. Thus, when two tori intersect in a conic section, the intersection curve must be a circle as well. Circles in a torus are classified into profile circles, cross-sectional circlet, and Yvone-Villarceau circles. Based on a geometric classification of these circles, we present a procedural method that can detect and construct all intersection circles between two tori. All computations can be carried out using simple geometric operations only: e.g., circle-circle intersections, circle-line intersections, vector additions, and inner products. Consequently, this simple structure makes our algorithm robust and efficient, which is an important advantage of our geometric approach over other conventional methods of surface intersection.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.559-568
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• 2018

Gene classification can involve complex order-restricted inference. Examining gene expression pattern across groups with order-restriction makes standard statistical inference ineffective and thus, requires different methods. For this problem, Roy's union-intersection principle has some merit. The M-estimator adjusting for outlier arrays in a microarray study produces a robust test statistic with distribution-insensitive clustering of genes. The M-estimator in conjunction with a union-intersection principle provides a nonstandard robust procedure. By exact permutation distribution theory, a conditionally distribution-free test based on the proposed test statistic generates corresponding p-values in a small sample size setup. We apply a false discovery rate (FDR) as a multiple testing procedure to p-values in simulated data and real microarray data. FDR procedure for proposed test statistics controls the FDR at all levels of ${\alpha}$ and ${\pi}_0$ (the proportion of true null); however, the FDR procedure for test statistics based upon normal theory (ANOVA) fails to control FDR.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• Journal of the Korea Computer Graphics Society
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• v.6 no.4
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• pp.29-34
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• 2000

This paper classifies the structure of the intersection curve between a surface of extrusion and a free-form surface. Our algorithm computes the silhouette curve of the free-form surface with respect to the unique ruling direction of the surface of extrusion. By intersecting the silhouette curve and the base curve of the surface of extrusion, we can classify the topological structure of the intersection curve, and compute all singularities in the intersection curve. Moreover, we can determine which ruling lines of the surface of extrusion intersect the other free-form surface and how many times. This classification provides a robust and efficient method for computing the intersection curve.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1418-1429
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• 2014

In this paper we suggest a new two-wheelers detection algorithm using local cell features. The first, we propose new feature vector matrix extraction algorithm using the correlation two cells based on local cell histogram and shifting from the result of histogram of oriented gradients(HOG). The second, we applied new weighting values which are calculated by the modified histogram intersection showing the similarity of two cells. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• Korean Journal of Computational Design and Engineering
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• v.7 no.2
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• pp.75-80
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• 2002

This paper details a robust and efficient algorithm for point classification with respect to a polygon in 2D real number domain. The concept of tolerance makes this algorithm robust and consistent. It enables to define‘on-boundary’ , which can be interpreted as either‘in-’or‘out-’side region, and to manage rounding errors in floating point computation. Also the tolerance is used as a measure of reliability of point classifications. The proposed algorithm is based on a ray-intersection technique known as the most efficient, in which intersections between a ray originating from a given test point and the boundary of a region are counted. An odd number of intersections indicates that the point is inside region. For practical examples the algorithm is most efficient because most edges of the polygon region are processed by simple bit operations.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.1097-1102
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• 2009

Classification of subjects with unknown distribution in small sample size setup may involve order-restricted constraints in multivariate parameter setups. Those problems makes optimality of conventional likelihood ratio based statistical inferences not feasible. Fortunately, Roy (1953) introduced union-intersection principle(UIP) which provides an alternative avenue. Redescending M-estimator along with that principle yields a considerably appropriate robust testing procedure. Furthermore, conditionally distribution-free test based upon exact permutation theory is used to generate p-values, even in small sample. Applications of this method are illustrated in simulated data and read data example (Lobenhofer et al., 2002)

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2321-2338
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• 2021

Compared with vehicle trajectories, pedestrian trajectories have stronger degrees of freedom and complexity, which poses a higher challenge to trajectory prediction tasks. This paper designs a mode to divide the trajectory of pedestrians at a traffic intersection, which converts the trajectory regression problem into a trajectory classification problem. This paper builds a deep model for pedestrian trajectory prediction at intersections for the task of pedestrian short-term trajectory prediction. The model calculates the spatial correlation and temporal dependence of the trajectory. More importantly, it captures the interactive features among pedestrians through the Attention mechanism. In order to improve the training speed, the model is composed of pure convolutional networks. This design overcomes the single-step calculation mode of the traditional recurrent neural network. The experiment uses Vulnerable Road Users trajectory dataset for related modeling and evaluation work. Compared with the existing models of pedestrian trajectory prediction, the model proposed in this paper has advantages in terms of evaluation indicators, training speed and the number of model parameters.