• Proceedings of the KIEE Conference
• /
• 2008.04a
• /
• pp.240-241
• /
• 2008

We are presenting the efficient and robust simplified module based self-collision detection of humanoid robot simulator. For safe and reliable operations of humanoid robot, the self-collision detection is essential and extremely important. The main methods of self-collision detection are inverse X-Y-Z fixed angles and module distance filtering (MDF). According to experiments on the humanoid robot simulator with the self-collision detection, we could have a confidence about the efficiency of the self-collision.

• Journal of Communications and Networks
• /
• v.14 no.3
• /
• pp.280-285
• /
• 2012

We propose self-encoded spread spectrum with two different iterative detection methods in multi-channel communication. The centralized iterative detection outperforms the iterative detection distributed over multiple channels. The results show that self-encoded spread spectrum with the centralized iterative detection is an excellent candidate for cognitive radio network.

• East Asian mathematical journal
• /
• v.38 no.1
• /
• pp.21-31
• /
• 2022

This paper is concerned with the optimal control problem associated to the self-organizing target detection model in 1D domains. That is, we show the global existence of weak solution and the existence of optimal control.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.08a
• /
• pp.153-153
• /
• 2012

Endpoint detection with plasma impedance monitoring and self-plasma optical emission spectroscopy is demonstrated for dielectric layers etching processes. For in-situ detecting endpoint, optical-emission spectroscopy (OES) is used for in-situ endpoint detection for plasma etching. However, the sensitivity of OES is decreased if polymer is deposited on viewport or the proportion of exposed area on the wafer is too small. To overcome these problems, the endpoint was determined by impedance signal variation from I-V monitoring (VI probe) and self-plasma optical emission spectroscopy. In addition, modified principal component analysis was applied to enhance sensitivity for small area etching. As a result, the sensitivity of this method is increased about twice better than that of OES. From plasma impedance monitoring and self-plasma optical emission spectroscopy, properties of plasma and chamber are analyzed, and real-time endpoint detection is achieved.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.13 no.5
• /
• pp.415-422
• /
• 2013

In this paper, we present Self-Checking look-up-table (LUT) based on Scalable Error Detection Coding (SEDC) scheme for use in fault-tolerant reconfigurable architectures. SEDC scheme has shorter latency than any other existing coding schemes for all unidirectional error detection and the LUT execution time remains unaffected with self-checking capabilities. SEDC scheme partitions the contents of LUT into combinations of 1-, 2-, 3- and 4-bit segments and generates corresponding check codes in parallel. We show that the proposed LUT with SEDC performs better than LUT with traditional Berger as well as Partitioned Berger Coding schemes. For 32-bit data, LUT with SEDC takes 39% less area and 6.6 times faster for self-checking than LUT with traditional Berger Coding scheme.

• Journal of Information Technology Applications and Management
• /
• v.25 no.2
• /
• pp.41-52
• /
• 2018

This paper presents a method for accurate camera self-calibration based on SIFT Feature Detection and image quality assessment. We performed image quality assessment to select high quality images for the camera self-calibration process. We defined high quality images as those that contain little or no blur, and have maximum contrast among images captured within a short period. The image quality assessment includes blur detection and contrast assessment. Blur detection is based on the statistical analysis of energy and standard deviation of high frequency components of the images using Discrete Cosine Transform. Contrast assessment is based on contrast measurement and selection of the high contrast images among some images captured in a short period. Experimental results show little or no distortion in the perspective view of the images. Thus, the suggested method achieves camera self-calibration accuracy of approximately 93%.

• Electronics and Telecommunications Trends
• /
• v.35 no.5
• /
• pp.30-42
• /
• 2020

Unlike in previous generations of mobile technology, machine learning (ML)-based self-healing research trend are currently attracting attention to provide high-quality, effective, and low-cost 5G services that need to operate in the HetNets scenario where various wireless transmission technologies are added. Self-healing plays a vital role in detecting and mitigating the faults, and confirming that there is still room for improvement. We analyzed the research trend in self-healing framework and ML-based fault detection, fault diagnosis, and fault compensation. We propose that to ensure that self-healing is a proactive instead of being reactive, we have to design an ML-based self-healing framework and select a suitable ML algorithm for fault detection, diagnosis, and outage compensation.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.4
• /
• pp.453-458
• /
• 2003

The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.5
• /
• pp.1463-1478
• /
• 2012

Based on the theory of local-world network, the composition self-similarity (CSS) of network traffic is presented for the first time in this paper for the study of DoS detection. We propose the concept of composition distribution graph and design the relative operations. The $(R/S)^d$ algorithm is designed for calculating the Hurst parameter. Based on composition distribution graph and Kullback Leibler (KL) divergence, we propose the composition self-similarity anomaly detection (CSSD) method for the detection of DoS attacks. We evaluate the effectiveness of the proposed method. Compared to other entropy based anomaly detection methods, our method is more accurate and with higher sensitivity in the detection of DoS attacks.

• Journal of KIISE:Software and Applications
• /
• v.37 no.7
• /
• pp.568-572
• /
• 2010

Self-healing is one of the techniques that assure dependability of mission-critical system. Self-healing consists of fault detection and fault recovery and fault detection is important first step that enables fault recovery but it causes overhead. We can detect fault based on model, the detection tasks that notify system's behavior and compare normal behavior model and system's behavior are heavy jobs. In this paper, we propose architecture-based multi-level self-adaptive monitoring method that complements model-based fault detection. The priority of fault detection per component is different in the software architecture. Because the seriousness and the frequency of fault per component are different. If the monitor is adapted to intensive to the component that has high priority of monitoring and loose to the component that has low priority of monitoring, the overhead can be decreased and the efficiency can be maintained. Because the environmental changes of software and the architectural changes bring the changes at the priority of fault detection, the monitor learns the changes of fault frequency and that is adapted to intensive to the component that has high priority of fault detection.