• ETRI Journal
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• v.41 no.2
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• pp.176-183
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• 2019

Spectrum sensing plays an important role in spectrum sharing. Energy detection is generally used because it does not require a priori knowledge of primary user (PU) signals; however, it is sensitive to noise uncertainty. An order statistics (OS) detector provides inherent protection against nonhomogeneous background signals. However, no analysis has been conducted yet to apply OS detection to spectrum sensing in a wireless channel to solve noise uncertainty. In this paper, we propose a robust spectrum sensing scheme based on generalized order statistics (GOS) and analyze the exact false alarm and detection probabilities under noise uncertainty. From the equation of the exact false alarm probability, the threshold value is calculated to maintain a constant false alarm rate. The detection probability is obtained from the calculated threshold under noise uncertainty. As a fusion rule for cooperative spectrum sensing, we adopt an OR rule, that is, a 1-out-of-N rule, and we call the proposed scheme GOS-OR. The analytical results show that the GOS-OR scheme can achieve optimum performance and maintain the desired false alarm rates if the coefficients of the GOS-OR detector can be correctly selected.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.669-670
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• 2008

In this paper, we present a real-time system to detect abnormal events on gas pipes, based on the signals which are observed through the audio sensors attached on them. First, features are extracted from these signals so that they are robust to noise and invariant to the distance between a sensor and a spot at which an abnormal event like an attack on the gas pipes occurs. Then, a classifier is constructed to detect abnormal events using neural networks. It is a combination of two neural network models, a Gaussian mixture model and a multi-layer perceptron, for the reduction of miss and false alarms. The former works for miss alarm prevention and the latter for false alarm prevention. The experimental result with real data from the actual gas system shows that the proposed system is effective in detecting the dangerous events in real-time with an accuracy of 92.9%.

• Journal of Navigation and Port Research
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• v.38 no.6
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• pp.645-653
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• 2014

A Bridge Navigational Watch Alarm System (hereafter 'BNWAS') is to monitor and detect if an officer of watch(hereafter 'OOW') keeps a sharp lookout on the bridge. The careless lookout of an OOW could lead to marine accidents. For this reason on June 5th, 2009, IMO decided that a ship is equipped with a BNWAS. However, an existing BNWAS gives the OOW a lot of inconvenience and stress in its operation. It requires that the OOW should press reset buttons to confirm their alert watch on the bridge at every three to twelve minute. Many OOWs have complained that at some circumstances they cannot focus on their bridge activities including watch-keeping due to a lots of resetting inputs of BNWAS. Accordingly, IMO has allowed the use of a motion sensor as a resetting device. The motion sensor detects the movements of human body on the bridge and subsequently sends reset signals directly to BNWAS automatically. As a result, OOWs can work uninterrupted. However, some of classification societies and flag authorities have a slightly different stance on the use of motion sensor as a resetting method for BNWAS. The reason is that the motion sensor may trigger false reset signals caused by the motion of objects on the bridge, especially a slight movement such as toss and turn of human body which can extend the period of careless watch. As a basic study to minimize the false reset signals, this paper proposes a simple configuration of BNWAS, which consists of only three motion sensors associated with 'AND' and 'OR' logic gates. Additionally, several considerations are also proposed for the implementation of motion sensors. This study found that the proposed configuration which consists of three motion sensors is better than an existing one by reducing false reset signals caused by a slight movement of human body in one's sleep. The proposed configuration in this paper filters false reset signals and is simple to be implemented on existing vessels. In addition, it can be easily installed just by a basic electrical knowledge.

• Proceedings of KOSOMES biannual meeting
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• 2005.05a
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• pp.7-12
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• 2005

A ship can be considered as a large human-machine system and the interaction between worker and system affects the work performed and its efficiency. Inside the bridge of a ship, there exist many auditory signals as well as visual signals. However, only a few studies have been performed related to human recognition to alarm systems in bridge. In this study, auditory icons and abstract sounds are compared to find more effective means of alarm systems. the study result shows tint auditory icons are recognized faster than abstract sounds. This result is expected to be used as a basic data for developing performance criteria of auditory display inside bridge and for designing integrated ship bridge alarm system.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.760-767
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• 2022

This paper introduces alert fatigue and presents methods to measure alert fatigue by using physiological signals. Alert fatigue is a phenomenon that which an individual is constantly exposed to frequent alarms and becomes desensitized to them. Blind spots are one leading cause of struck-by accidents, which is one most common causes of fatal accidents on construction sites. To reduce such accidents, construction equipment is equipped with an alarm system. However, the frequent alarm is inevitable due to the dynamic nature of construction sites and the situation can lead to alert fatigue. This paper introduces alert fatigue and proposes methods to use physiological signals such as electroencephalography, electrodermal activity, and event-related potential for the measurement of alert fatigue. Specifically, this paper presents how raw data from the physiological sensors measuring such signals can be processed to measure alert fatigue. By comparing the processed physiological data to behavioral data, validity of the measurement is tested. Using preliminary experimental results, this paper validates that physiological signals can be useful to measure alert fatigue. The findings of this study can contribute to investigating alert fatigue, which will lead to lowering the struck-by accidents caused by blind spots.

• Journal of Ecology and Environment
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• v.30 no.1
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• pp.9-15
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• 2007

Black-tailed gulls (Larus crassirostris) produce alarm calls apparently related to their anti-predator behaviors, but the hypothesis that the calls are actually used as functionally referential alarm signals has not yet been tested. In this study, we performed a series of experiments using visual (a stuffed goshawk: Accipiter gentilis) and acoustic (alarm calls and a control vocalization) stimuli at 15 sites in Sinjindo-ri and Dowhang-ri, Taean-gun, Chungnam province to examine anti-predator responses of the gulls to alarm calls in playback trials. We found that the gulls' visual recognition of a perched hawk model in the absence of alarm vocalizations was weak or absent because the model was noticed in only two out of 16 trials. The gulls' responses to playbacks of the alarm call only and the alarm call with a visual stimulus differed from responses to the control vocalization in latency to approach, time mobbing, and the percentage of gulls responding, while the responses to alarm call only differed from alarm call with a visual stimulus in latency to first fly, latency to call, and time mobbing. The results of this study suggest that alarm calls of black-tailed gulls are used to elicit appropriate anti-predator behaviors that are intensified when a predator is detected visually.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.107.1-107.1
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• 2014

We apply a machine learning algorithm, artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. We also evaluate the gravitational-wave data within a few seconds of the selected short gamma-ray bursts' event times using the trained networks and obtain the false alarm probability. We suggest that artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.

• The KIPS Transactions:PartC
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• v.11C no.3
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• pp.301-308
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• 2004

Misuse IDS is known to have an acceptable accuracy but suffers from high rates of false alarms. We show a behavior based alarm reduction with a memory-based machine learning technique. Our extended form of IBL, (XIBL) examines SNORT alarm signals if that signal is worthy sending signals to security manager. An experiment shows that there exists an apparent difference between true alarms and false alarms with respect to XIBL behavior This gives clear evidence that although an attack in the network consists of a sequence of packets, decisions over Individual packet can be used in conjunction with misuse IDS for better performance.

• Journal of the Korean Society of Marine Environment & Safety
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• v.11 no.1 s.22
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• pp.11-16
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• 2005

A ship can be considered as a large human-machine system and the interaction between worker and system affects the work performance and its efficiency. In the bridge if a ship, there exist many auditory signals as well as visual signals. However, only a few studies have been performed related to human recognition to alarm systems in the bridge. In this study, auditory icons and abstract sounds are compared to find more effective means if alarm systems. The study result shows that auditory icons are recognized faster than n abstract sounds. The result is expected to be use as a basic data for developing performance criteria q auditory display inside bridge and for designing integrated ship bridge alarm system.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.634-639
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• 2014

This paper specifies a collision alarm using an infrared distance sensor that can identify the dangerousness of collision of active mobile robotic systems to various objects, such as unknown objects or another robots. And we analyse the major operating signals and features of the collision alarm for effective industrial applications. For the purpose, we consider a typical parking situation of a mobile robotic system with the collision alarm designed. As a result, it is shown that the proposed collision alarm is applicable for effective collision avoidance and safe driving of various mobile robots or vehicles.