• Asian Journal of Innovation and Policy
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• v.2 no.1
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• pp.20-36
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• 2013

In this practice article, we present the results of a scenario planning approach that is a hybrid of the three main schools of thought. Our research objective was to study the future of Interactive Digital Media applications such as online music, on-demand television and massively multi-player online role-playing games. Our approach, while essentially qualitative in nature, nevertheless draws from the rigors of the quantitative school in identifying and then tracking the significant dimensions of analysis that emerge over time as strands of events leading to plausible scenarios. Our empirical analysis revealed mapping strands to three themes - ownership, distribution and innovation - which we used in an expert validation exercise to formulate scenarios. We present and discuss the major findings and implications of this empirical investigation. In a nutshell, we conjecture that an open, competitive IDM marketplace with performance safeguards may serve both and lead to a win-win scenario. While there are differences among IDM sectors, a unified approach to regulation and policy would be effective.

• Journal of Information Technology Services
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• v.4 no.2
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• pp.61-69
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• 2005

RFID is used for tracking systems in various business fields these days and these systems brought considerable efficiencies and cost savings to companies. Real-time based information acquired through RFID devices could be a valuable source of information for making decisions if it is combined with decision support tools like OLAP of a data warehouse that has originally been designed for analyzing static and historical data. As an effort of extending the data source of a data warehouse, RFID is combined with a data warehouse in this research. And OLAP is designed to analyze the dynamic real-time based information gathered through RFID devices. The implemented prototype shows that ubiquitous computing technology such as RFID could be a valuable data source for a data warehouse and is very useful for making decisions when it is combined with online analysis. The system architecture of such system is suggested.

• Journal of Power Electronics
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• v.17 no.1
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• pp.149-160
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• 2017

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

• Convergence Security Journal
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• v.24 no.3
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• pp.23-31
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• 2024

The anonymity and decentralized nature of cryptocurrencies make them highly susceptible to criminal exploitation, requiring the development of effective tracking techniques. By analyzing various open source intelligence(OSINT), such as public data, social media, and online forums, open source intelligence can provide useful information for identifying criminals and tracking the flow of cryptocurrency funds. In this study, we present a comprehensive proposal for the utilization of open source intelligence. We will discuss the current status and trends of cryptocurrency and related crimes, and introduce the concept and methodology of open source intelligence. The paper then focuses on five methods and seven frameworks of open source intelligence for tracking and analyzing cryptocurrency-related crimes, and presents techniques for the integrated application of open source intelligence methods and frameworks.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.54-60
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• 2020

Online cybercrime has various causes. The criminal guilty language, Criminal lingo is active in the shaded area with the bilateral aspect of the word on cyber. It has been continuously producing massive risk factors in cyberspace. Criminals are shared and disseminated online. It has been linked with fake news and aids to suicide that has recently become an issue. Thus the criminal lingo has become a real danger factor on cyber interface. Recently, Criminal lingo is shared and distributed as cyber hazard information. It is transformed that damaging to the youth and ordinary people through the internet and social networks. In order to take action, it is necessary to construct an expert system based on AI to implement a smart management architecture with block-chain technology. In this paper, we study technically a new smart management architecture which uses artificial intelligence based decision algorithm and block-chain tracking technology to prevent the spread of criminal lingo factors in the evolving cyber world. In addition, through the off-line regular patrol program of police units, we proposed the conversion of online regular patrol program for "cyber harem area".

• The Journal of Korean Physical Therapy
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• v.22 no.6
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• pp.43-51
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• 2010

Purpose: This study examined the influence of visuomotor congruency on learning a relative phase relationship between a single joint movement and an external signal. Methods: Participants (N=5) were required to rhythmically coordinate elbow flexion-extension movements with a continuous sinusoidal wave (0.375 Hz) at a $90^{\circ}$ relative phase relationship. The congruent group was provided online feedback in which the elbow angle decreased (corresponding to elbow flexion) as the angle trajectory was movingup, and vice versa. The incongruent group was provided online feedback in which the elbow angle decreased as the angle trajectory was moving down, and vice versa. There were two practice sessions (day 1 and 2) and each session consisted of 6 trials per block (5 blocks per session). Retention tests were performed 24 hours after session 2, and only the external sinusoidal wave was provided. Repeated ANOVAs were used for statistical analysis. Results: During practice, the congruent group was significantly less variable than the incongruent group. Phase variability in the incongruent group did not significantly change across blocks, while variability decreased significantly in the congruent group. In retention, the congruent group produced the required $90^{\circ}$ relative phase pattern with significantly less phase variability than the incongruent group. Conclusions: Congruent visual feedback facilitates learning. Moreover, the deprivation of online feedback does not affect the congruent group but does affect the incongruent group in retention.

• ETRI Journal
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• v.43 no.3
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• pp.459-470
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• 2021

This paper presents a low-cost prototype for monitoring online the maximum power produced by a domestic photovoltaic (PV) system using Internet of Things (IoT) technology. The most common tracking algorithms (P&O, InCond, HC, VSS InCond, and FL) were first simulated using MATLAB/Simulink and then implemented in a low-cost microcontroller (Arduino). The current, voltage, load current, load voltage, power at the maximum power point, duty cycle, module temperature, and in-plane solar irradiance are monitored. Using IoT technology, users can check in real time the change in power produced by their installation anywhere and anytime without additional effort or cost. The designed prototype is suitable for domestic PV applications, particularly at remote sites. It can also help users check online whether any abnormality has happened in their system based simply on the variation in the produced maximum power. Experimental results show that the system performs well. Moreover, the prototype is easy to implement, low in cost, saves time, and minimizes human effort. The developed monitoring system could be extended by integrating fault detection and diagnosis algorithms.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.355-362
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• 2010

Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.177-182
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• 1989

In this paper a new adaptive control algorithm is derived, with the unknown manipulator and payload parameters being estimated online. In practice, we may simplify the algorithm by not explicity estimating all unknown parameters. Further, the controller must be robust to residual time-varying disturbance, such as striction or torque ripple. Also, the reference model is a simple douple integrator and the acceleration input for robot manipulator consists of a proportion and derivative controller for trajectory tracking purposes. The validity of this control is confirmed in simulation where two-link robot manipulator shows the robust performances in spite of the existing nonlinear interaction and unknown parametrictings

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.11-12
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• 2017

본 논문에서는 부류가 정해진 훈련 집합이 불필요한 온라인 학습 기반 추적 기법을 제안한다. 추적기의 학습을 위해 합성곱 신경망(convolutional neural network: CNN)을 이용하였다. 추적영상으로부터 직접 훈련 샘플을 수집함으로써 분류기 학습을 위한 비용을 감소시킬 수 있었고, 목표 영상에 적응적인 객체 모델을 생성할 수 있다. 실험 결과를 통해 제안하는 방법이 우수한 성능을 보임을 입증하였다.