• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.1-7
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• 2021

Detect and avoid (DAA) system, which is essential for the operation of UAS, detects intruding aircraft and offers the ranges of turn and climb/descent maneuver that are required to avoid the intruder. This paper uses detect and avoid alerting logic for unmanned systems (DAIDALUS) developed at NASA as a DAA algorithm. Since DAIDALUS offers ranges of avoidance maneuvers, the actual avoidance maneuver must be decided by the UAS pilot as well as the timing and method of returning to the original route. It can be readily used in real-time human-in-the-loop (HiTL) simulations where a human pilot is making the decision, but a pilot decision model is required in fast-time simulations that proceed without human pilot intervention. This paper proposes a pilot decision model that maneuvers the aircraft based on the DAIDALUS avoidance maneuver range. A series of tests were conducted using test vectors from radio technical commission for aeronautics (RTCA) minimum operational performance standards (MOPS). The alert levels differed by the types of encounters, but loss of well clear (LoWC) was avoided. This model will be useful in fast-time simulation of high-volume traffic involving UAS.

• Journal of radiological science and technology
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• v.37 no.3
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• pp.177-185
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• 2014

This study focused on effects of patient exposure dose reduction with AEC (Auto Exposure Control) marker that is designed for showing location of AEC in X-ray Chest radiography. It included 880 adults who have to use Chest X-ray Digital Radiography system (DRS, LISTEM, Korea). AEC (Ion chambers are posited in top of both sides) are used to every adult and set X-ray system as Field size $17{\times}17inch$, 120kVp, FFD 180cm. 440 people of control group are posited on detector to include both sides of lung field and the other 440 people of experimental group are set to contact their lung directly to Ion chamber (making marker to shows location). Then, measured every DAP and, estimated patient effective dose by using PCXMC 2.0. The average age of control group (M:F=245:195) is 53.9 and the average BMI is 23.4. BMI ranges from under weight: 35, normal range: 279, over weight: 106 to obese: 20 and average DAP is 223.56mGycm2, Mean effective dose is 0.045mSv. The average age of experimental group (M:F=197:243) is 53.7 and the average BMI is 22.7. BMI ranges from under weight: 34, normal range: 315, over weight: 85 to obese: 6 and average DAP is 207.36mGycm2, Mean effective dose is 0.041mSv. Experimental group shows less Mean effective dose as 0.004mSv (9.7%) than control group. Also, patient numbers who got over exposure more than 0.056mSv (limit point to know efficiency of AEC marker) is 65 in control group (14.7%), 19 in experimental group (4.3%) and take statistics with t-Test. The statistical difference between two groups is 0.006. In order to use proper amount of X-ray in auto exposure controlled chest X-ray system, matching location between ion chamber and body part is needed, and using AEC marker (designed for showing location of ion chamber) is a way to reduce unnecessary patient exposure dose.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.8
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• pp.62-71
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• 2001

This paper presents a design of the real-time digital image enhancement preprocessor for CMOS image sensor. CMOS image sensor offers various advantages while it provides lower-quality images than CCD does. In order to compensate for the physical limitation of CMOS sensor, the spatially adaptive contrast enhancement algorithm was incorporated into the preprocessor with color interpolation, gamma correction, and automatic exposure control. The efficient hardware architecture for the preprocessor is proposed and was simulated in VHDL. It is composed of about 19K logic gates, which is suitable for low-cost one-chip PC camera. The test system was implemented on Altera Flex EPF10KGC503-3 FPGA chip in real-time mode, and performed successfully.

• The Korean Journal of Pesticide Science
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• v.10 no.2
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• pp.138-148
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• 2006

Two bacteria were isolated from the continuously pesticide-used soil under plastic film house and upland condition. The degradation test of several pesticides by the selected bacteria, B59 and B71, were conducted. The degradation rates for 6 pesticides, procymidone, chlorothalonil, ethoprophos parathior, alachlor and pendimethalin, in medium by the isolates were 21.1% to 53.2% higher than non-inoculated medium. Under shaking culture condition, 90% to 95% of procymidone was degraded after 21 days treatment. Parathion was degraded in the range of 60% to 100% by B71 and B59, respectively. Otherwise 70% of alachlor was degraded by the two isolated bacteria during same period. The pH was not significantly affected for degradation of pesticides. The bacterial strains, B59 and B71 was identified as Acinetobacter sp. and as Pseudomonas sp. based on morphological, biochemical and physiological characteristics, and identity and similarity of automatic identification system, Biolog and MIDI.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.278-284
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• 2003

Speech act is the speaker's intentions indicated through utterances. It is important for understanding natural language dialogues and generating responses. This paper proposes the method of two stage that increases the performance of the korean speech act decision. The first stage is to select features from the part of speech results in sentence and from the context that uses previous speech acts. We use x$^2$ statistics(CHI) for selecting features that have showed high performance in text categorization. The second stage is to determine speech act with selected features and Neural Network. The proposed method shows the possibility of automatic speech act decision using only POS results, makes good performance by using the higher informative features and speed up by decreasing the number of features. We tested the system using our proposed method in Korean dialogue corpus transcribed from recording in real fields, and this corpus consists of 10,285 utterances and 17 speech acts. We trained it with 8,349 utterances and have test it with 1,936 utterances, obtained the correct speech act for 1,709 utterances(88.3%). This result is about 8% higher accuracy than without selecting features.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.221-227
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• 2013

RFID is an automatic identification technology that can control a range of information via IC chips and radio communication. Also known as electronic tags, smart tags or electronic labels, RFID technology enables embedding the overall process from production to sales in an ultra-small IC chip and tracking down such information using radio frequencies. Currently, RFID-based application and development is in progress in such fields as health care, national defense, logistics and security. RFID structure consists of a reader that reads tag information, a tag that provides information and the database that manages data. Yet, the wireless section between the reader and the tag is vulnerable to security issues. To sort out the vulnerability, studies on security protocols have been conducted actively. However, due to difficulties in implementation, most suggestions are concerned with theorem proving, which is prone to vulnerability found by other investigators later on, ending up in many troubles with applicability in practice. To experimentally test the security of the protocol proposed here, the formal verification tool, CasperFDR was used. To sum up, the proposed protocol was found to be secure against diverse attacks. That is, the proposed protocol meets the safety standard against new types of attacks and ensures security when applied to real tags in the future.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.153-176
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• 1993

Structual ambiguity is one of those problem that arise in the analysis of natural language sentences.It has been considered very difficult to solve the problem.Structural ambiguity,however,should be resolved no matter how difficult it may be.Otherwise natural language processing could be virtually impossible.A statistical approach to structural disambiguation is proposed in this dissertation.The information-theoretic concept of mutual information has been empolyed in resolving structural ambiguity Mutual information can be acquired in an automatic way.from text corpora. If a structural disambiguation subsystem had the capability of self-evaluating whether the results of structural disambiguation are correct or not.it would be possible to develop a more intelligent natural language proessing system.In this paper,the concept of confidence measure is also proposed to endow the disambiguation subsystem with such intelligence.Confidence measure is a numeric value calculated after structural disambiguation. Some experiments were performed in order to show the validity of the approach.Mutual information was auto matically acquired from a corpus of 1.6milion words that were collected from scientific abstracts.The accuracy of structural disambiguation was 80%when performed over 1,639 test sentences.Notice that there was no manual tuning in advance for the experiments.The task of detecting and correcting errors in structural disambiguation will be performed very effectively if the concept of confidence measure is employed in the process.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.177-183
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• 2002

Current state-of-the-art of VMS control is based upon simple knowledge-based inference engine with message set and each message's priority. And R&Ds of the VMS control are focused on the accurate detection and estimation of traffic condition of the subject roadways. However VMS display itself cannot achieve a desirable traffic allocation among alternative routes in the network In this context, VMS display strategy is the most crucial part in the VMS control. VMS itself has several limitations in its nature. It is generally known that VMS causes overreaction and concentration problems, which may be more serious in urban network than highway network because diversion should be more easily made in urban network. A feedback control algorithm is proposed in this paper to address the above-mentioned issues. It is generally true that feedback control approach requires low computational effort and is less sensitive to models inaccuracy and disturbance uncertainties. Major features of the proposed algorithm are as follows: Firstly, a regulator is designed to attain system optimal traffic allocation among alternative routes for each VMS in the network. Secondly, strategic messages should be prepared to realize the desirable traffic allocation, that is, output of the above regulator. VMS display strategy module is designed in this context. To evaluate Probable control benefit and to detect logical errors of the Proposed feedback algorithm, a offline simulation test is performed using real network in Daejon, Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.