• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1500-1503
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• 1997

In this paper a multiple channel EPD controller is developed which enables us to detect endpoints simultaneously in the plasma etching process operated in multiple etching chambers and its performance characteristic are investigated. for the accurate detectiion of endpoint the developed EDP controller was able to implement endpoint detectiions by integrating the existing EPD controllers with the techiques of artificial intellignet, to enhance its performance. The performance of the developed EPD controller was carried out by repeated experiments of endpoint detection in the acrual production line of semiconductor manufacturing. It's utility for endpoint detectiion was accurately evaluated in various etching process. The control capability of multiple etching chambers enhances its application compared with the existing one, and also increases the user utility os that the efficiency of operation was improved.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1291-1295
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• 2004

This paper proposed a new approach for endpoint detection of isolated speech, which proves to significantly improve the endpoint detection performance. The proposed algorithm relies on the root mean square energy (rms energy), zero crossing rate and spectral characteristics of the speech signal where the Euclidean distance measure is adopted using cepstral coefficients to accurately detect the endpoint of isolated speech. The algorithm offers better performance than traditional energy-based algorithm. The vocabulary for the experiment includes English digit from one to nine. These experimental results were conducted by 360 utterances from a male speaker. Experimental results show that the accuracy of the algorithm is quite acceptable. Moreover, the computation overload of this algorithm is low since the cepstral coefficients parameters will be used in feature extraction later of speech recognition procedure.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.111-117
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• 2009

In this paper, a new speech endpoint detector in noisy environment is proposed. According to the previous research, the energy feature in the speech region is easily distinguished from that in the speech absent region. In conventional method, the endpoint can be found by applying the edge detection filter that finds the abrupt changing point in feature domain. However, since the frame energy feature is unstable in noisy environment, the accurate edge detection is not possible. Therefore, in this paper, the novel feature extraction method based on spectrum envelop pattern is proposed. Then, the edge detection filter is applied to the proposed feature for detection of the endpoint. The experiments are performed in the car noise environment and a substantial improvement was obtained over the conventional method.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.57-64
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• 1999

In this paper, we investigated the robust endpoint detection algorithm in noisy environment. A new feature parameter based on a discrete wavelet transform is proposed for word boundary detection of isolated utterances. The sum of standard deviation of wavelet coefficients in the third coarse and weighted first detailed scale is defined as a new feature parameter for endpoint detection. We then developed a new and robust endpoint detection algorithm using the feature found in the wavelet domain. For the performance evaluation, we evaluated the detection accuracy and the average recognition error rate due to endpoint detection in an HMM-based recognition system across several signal-to-noise ratios and noise conditions.

• The Journal of the Acoustical Society of Korea
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• v.26 no.1E
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• pp.21-26
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• 2007

In this paper, we propose a modified Viterbi algorithm to compensate for endpoint detection error during the decoding phase of an isolated word recognition task. Since the conventional Viterbi algorithm explores only the search space whose boundaries are fixed to the endpoints of the segmented utterance by the endpoint detector, the recognition performance is highly dependent on the accuracy level of endpoint detection. Inaccurately segmented word boundaries lead directly to recognition error. In order to relax the degradation of recognition accuracy due to endpoint detection error, we describe an unconstrained search of word boundaries and present an algorithm to explore the search space with efficiency. The proposed algorithm was evaluated by performing a variety of simulated endpoint detection error cases on an isolated word recognition task. The proposed algorithm reduced the Word Error Rate (WER) considerably, from 84.4% to 10.6%, while consuming only a little more computation power.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.123.2-123.2
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• 2013

Etching endpoint detection with plasma impedance monitoring (PIM) is demonstrated for small area dielectric layers inductive coupled plasma etching. The endpoint is determined by the impedance harmonic signals variation from the I-V monitoring system. Measuring plasma impedance has been examined as a relatively simple method of detecting variations in plasma and surface conditions without contamination at low cost. Cluster analysis algorithm is modified and applied to real-time endpoint detection for sensitivity enhancement in this work. For verification, the detected endpoint by PIM and real-time cluster analysis is compared with widely used optical emission spectroscopy (OES) signals. The proposed technique shows clear improvement of sensitivity with significant noise reduction when it is compared with OES signals. This technique is expected to be applied to various plasma monitoring applications including fault detections as well as end point detection.

• Journal of the Korean Society of Safety
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• v.38 no.6
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• pp.60-71
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• 2023

The purpose of this study is to propose a quantitative toxicity endpoint distance suitable for the initial response of firefighters by comparing and analyzing the commonly applied toxic level of concern (T-LOC), specifically emergency response planning guidelines (ERPG), acute exposure guideline levels (AEGL), and immediately dangerous to life or health (IDLH). This is to protect the fire brigade, which responds to toxic chemical accidents first during the golden time. Using areal locations of hazardous atmospheres, a damage prediction program, the amount of leakage for both acidic and basic substances, along with the endpoint distance, were analyzed for alternative accident and worst-case accident scenarios. The results showed that the toxicity endpoint distance, serving as a compromise between Level-3 and Level-2 of T-LOC, was longer than ERPG-3 and shorter than ERPG-2 with IDLH, while its values were analyzed in the order of ERPG-2, AEGL-2, IDLH, AEGL-3, and ERPG-3. It is suggested that the application of IDLH in an emergency (red card) and ERPG-2 endpoint distance in a non-emergency (non-red card) can be utilized for the initial response of the fire brigade.

• Journal of IKEEE
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• v.19 no.4
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• pp.541-547
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• 2015

In current semiconductor manufacturing, as the feature size of integrated circuit (IC) devices continuously shrinks, detecting endpoint in plasma etching process is more difficult than before. For endpoint detection, various kinds of sensors are installed in semiconductor manufacturing equipments, and sensor data are gathered with predefined sampling rate. Generally, detecting endpoint is performed using OES data of by-product. In this study, OES data of both by-product and etchant gas are used to improve reliability of endpoint detection. For the OES data pre-processing, a combination of Signal to Noise Ratio (SNR) and Principal Component Analysis (PCA),are used. Polynomial Regression and Expanded Hidden Markov model (eHMM) technique are applied to pre-processed OES data to detect endpoint.

• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.147-156
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• 2013

In this paper, a new speech endpoint detection method in noisy environments for moving robot platforms is proposed. In the conventional method, the endpoint of speech is obtained by applying an edge detection filter that finds abrupt changes in the feature domain. However, since the feature of the frame energy is unstable in such noisy environments, it is difficult to accurately find the endpoint of speech. Therefore, a novel feature extraction method based on the twice-iterated fast fourier transform (TIFFT) and statistical models of speech is proposed. The proposed feature extraction method was applied to an edge detection filter for effective detection of the endpoint of speech. Representative experiments claim that there was a substantial improvement over the conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4909-4926
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• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.