• Journal of Korea Game Society
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• v.11 no.1
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• pp.121-129
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• 2011

In an online-game, the various game service victimized cases are generated by the bots program or auto program. Particularly, the abnormal collection of the game money and item loses the inherent fun of a game. It reaches ultimately the definite bad effect to the game life cycle. This paper collects and analyzes the pattern of game behavior change for the bots detection method. By using the game activity changing information of the human and game activity changing information of the bots, the degree of resemblance was measured. It utilized in the bots detection method. In an experiment, by using the served online-game, the model of a user and bots were generated and similarity was distinguished. And the reasonable result was confirmed.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.705-708
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• 1988

Ultrasound Doppler Diagnostic System utilizes the Doppler effect for measurement of blood velocity. The sign of the Doppler frequency shift represents blood flow direction. Pulsed Doppler System uses Phase detector and zerocrossing method to produce simultaneous independent audio and velocity signals for forward and reverse blood flow direction in the time domain, had been fabricated. But time-domain analyzing such as audio evaluation and zerocrossing detection for instantaneous and mean frequency measurement doesn't, provide both an accurate and quantitative result. Therefore, it is necessary to adopt frequency domain technique to improve system performance. In this paper, we describe a unit which is composed of Pulsed Doppler System and real-time spectrum analyzer (installed TMS 32010 DSP Chip). This unit shows time-dependent spectrum variation and mean velocity of blood Signal.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2268-2271
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• 2002

NLPCA(Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the weil-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(AutoAssociative Neural Network) which performs the identity mapping. In this work, a sensor fault defection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from Saemangeum measurement stations is executed.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.3
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• pp.87-94
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• 2017

GNSS modernization and development is in progress throughout the globe, and it is focused on the addition of a new navigation signal. Accordingly, for the next-generation GNSS signals that have been developed or are under development, various combinations that are different from the existing GNSS signal structures can be introduced. In this regard, to design an advanced signal, it is essential to clearly understand the effects of the signal structure and design variables. In the present study, the effects of the GNSS spreading code period and GNSS data bit duration (i.e., signal design variables) on the signal processing performance were analyzed when the data bit transition was considered, based on selected GNSS signal design scenarios. In addition, a method of utilizing the obtained result for the design of a new GNSS signal was investigated.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.6-15
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• 2022

In this study, a traffic light recognition algorithm was implemented and validated for low-speed special purpose vehicles in an urban environment. Real-time image data using a camera and YOLO algorithm were applied. Two methods were presented to increase the accuracy of the traffic light recognition algorithm, and it was confirmed that the second method had the higher accuracy according to the traffic light type. In addition, it was confirmed that the optimal YOLO algorithm was YOLO v5m, which has over 98% mAP values and higher efficiency. In the future, it is thought that the traffic light recognition algorithm can be used as a dual system to secure the platform safety in the traffic information error of C-ITS.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.3
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• pp.42-51
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• 1999

An experimental modeling of cutting and structural dynamics and the on-line detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics of cutting process but also for the analytic realization of diagnostic systems. In this regard, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision round shape machining such as turning, drilling and boring in mold and die making. In this study, simulation and experimental work were performed to show the malfunctioned behaviors. For this purpose, two new recursive approach (REIVM, RLSM) were adopted fur the on-line system identification and monitoring of a machining process, we can apply these new algorithm in real process for the detection of abnormal machining behaviors such as chipping, chatter, wear and round shape lobe waviness.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2289-2291
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• 2003

NLPCA(Nonlinear Principal Component Analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA can be implemented by a feedforward neural network called AANN (AutoAssociative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA and Maximum Likelihood Estimation scheme is presented. To verify its applicability, simulation study on the data supplied from Saemangeum measurement stations is executed.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.8
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• pp.49-56
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• 1997

In recent years, ultrasonic technics has been widely applied to industrial fields and its application range has been expanded as a result of continuous research and development. This paper is concerned with development of post-processor program for ultrasonic test and two-axis automatic ultrasonic system for application of industrial fields. Test results of ultrasonic test post-processor program and two-axis auto- matic ultrasonic system have a good agreement with results of ultrasonic evaluation for defect detection in industrial fields. Therefore we think that the developed ultrasonic test post-processor program and two- axis automatic ultrasonic system in this work is very useful for application of industrial fields.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1551-1559
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• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.