• Journal of Welding and Joining
• /
• 제17권6호
• /
• pp.90-99
• /
• 1999

Significant portion of the total manufacturing time for a pipe fabrication process is spent on the welding following primary machining and fit-up processes. To achieve a reliable weld bead appearance, automatic seam tracking and adaptive control to fill the groove are urgently needed. For the seam tracking in welding processes, the vision sensors have been successfully applied. However, the adaptive filling control of the multi-torches system for the appropriate welded area has not been implemented in the area of SAW(submerged arc welding) by now. The term adaptive control is often used to describe recent advances in welding process control by strictly this only applies to a system which is able to cope with dynamic changes in system performance. In welding applications, the term adaptive control may not imply the conventional control theory definition but may be used in the more descriptive sense to explain the need for the process to adapt to the changing welding conditions. This paper proposed various types of methodologies for obtaining a good bead appearance based on multi-torches welding system with the vision system in SAW. The methodologies for adaptive filling control used welding current/voltage, arc voltage/welding current/wire feed speed combination and welding speed by using vision sensor. It was shown that the algorithm for welding current/voltage combination and welding speed revealed sound weld bead appearance compared with that of voltage/current combination.

• 한국조명전기설비학회:학술대회논문집
• /
• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
• /
• pp.309-314
• /
• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 인터넷정보학회논문지
• /
• 제24권4호
• /
• pp.37-45
• /
• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.

• Earthquakes and Structures
• /
• 제12권3호
• /
• pp.271-284
• /
• 2017

This paper presents a multimodal adaptive nonlinear static method of analysis that, differently from the nonlinear static methods suggested in seismic codes, does not require the definition of the equivalent Single-Degree-Of-Freedom (SDOF) system to evaluate the seismic response of structures. First, the proposed method is formulated for the assessment of r.c. plane frames and then it is extended to 3D framed structures. Furthermore, the proposed nonlinear static approach is re-elaborated as a displacement-based design method that does not require the use of the behaviour factor and takes into account explicitly the plastic deformation capacity of the structure. Numerical applications to r.c. plane frames and to a 3D framed structure with inplan irregularity are carried out to illustrate the attractive features as well as the limitations of the proposed method. Furthermore, the numerical applications evidence the uncertainty about the suitability of the displacement demand prediction obtained by the nonlinear static methods commonly adopted.

• 전기학회논문지P
• /
• 제56권2호
• /
• pp.74-82
• /
• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning control fuzzy neural network (AFLC-FNN) controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC-FNN controller. Also, this paper proposes speed control of IPMSM using AFLC-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• 한국콘텐츠학회논문지
• /
• 제12권3호
• /
• pp.1-8
• /
• 2012

비디오 프레임 율 증가 변환은 가전 분야에서 매우 다양한 응용으로 인해 매우 많은 관심을 받아 오고 있다. 대 부분의 진보된 FRUC 알고리즘은 보간된 프레임들의 움직임 벡터장을 결정하는 움직임 보간 기술을 사용하고 있다. 그러나 몇 개의 응용 분야에서는 움직임 보상 보간 프레임이 얼마나 잘 복원되었는지에 대한 정보를 필요로 한다. 이와 같은 목적을 위해 본 논문에서는 프레임 기반의 적응적 예측에 기초한 움직임 보상 보간 프레임의 왜곡 예측 기법을 제안한다. 제안된 기법은 대칭형 움직임 탐색 및 보상 보간 기법에 적용되며, 세 가지 다른 예측 기법 즉, 순방향, 역방향 그리고 적응적 양방향 예측 기법으로 분석된다. 모의 실험을 통하여 제안된 적응적 양방향 왜곡 예측 방식이 다른 두 방식에 비해 성능이 우수함을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권12호
• /
• pp.5805-5825
• /
• 2019

Smartphones are important platforms because of their sophisticated computation, communication, and sensing capabilities, which enable a variety of applications in the Internet of Things (IoT) systems. Moreover, advancements in hardware have enabled sensors on smartphones such as environmental and chemical sensors that make sensor data collection readily accessible for a wide range of applications. However, dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users that vary throughout the day, which greatly affects the efficacy of sensor data collection. Therefore, it is necessary to consider phone users mobility patterns to design data collection schedules that can reduce the loss of sensor data. In this paper, we propose a mobility-based weighted adaptive opportunistic scheduling framework that can adaptively adjust to the dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users and provide prioritized scheduling based on various application scenarios, such as velocity, region of interest, and sensor type. The performance of the proposed framework is compared with other scheduling frameworks in various heterogeneous smartphone user mobility scenarios. Simulation results show that the proposed scheduling improves the transmission rate by 8 percent and can also improve the collection of higher-priority sensor data compared with other scheduling approaches.

• ETRI Journal
• /
• 제33권3호
• /
• pp.423-433
• /
• 2011

This paper introduces an adaptive and integrated utterance verification (UV) framework using minimum verification error (MVE) training as a new set of solutions suitable for real applications. UV is traditionally considered an add-on procedure to automatic speech recognition (ASR) and thus treated separately from the ASR system model design. This traditional two-stage approach often fails to cope with a wide range of variations, such as a new speaker or a new environment which is not matched with the original speaker population or the original acoustic environment that the ASR system is trained on. In this paper, we propose an integrated solution to enhance the overall UV system performance in such real applications. The integration is accomplished by adapting and merging the target model for UV with the acoustic model for ASR based on the common MVE principle at each iteration in the recognition stage. The proposed iterative procedure for UV model adaptation also involves revision of the data segmentation and the decoded hypotheses. Under this new framework, remarkable enhancement in not only recognition performance, but also verification performance has been obtained.

• 한국인터넷방송통신학회논문지
• /
• 제15권3호
• /
• pp.119-124
• /
• 2015

최근 스마트폰 앱의 위치 기반 서비스 사용이 늘어남에 따라 GPS로 인한 배터리 소모가 심각한 수준에 이르고 있다. 본 논문에서는 배터리 효율적인 위치 탐지 기법인 ALD(Adaptive Location Detection) 기법을 제안한다. ALD는 스마트폰 사용자의 이동 패턴과 실행 중인 앱의 특성, 잔여 배터리 수준 등에 따라 적절한 위치 추정 방법을 실시간으로 전환하여 위치 기반 서비스의 전력소모를 극소화한다. 다양한 실제 안드로이드 앱과 가상 시나리오 환경에서의 시뮬레이션 결과 제안한 방법이 GPS에 비해 전력소모를 평균 37% 줄임을 확인하였다. 그럼에도 각 앱이 필요로 하는 위치 정확성을 제공함을 검증하였다.

• 전기학회논문지
• /
• 제57권6호
• /
• pp.1036-1041
• /
• 2008

Periodic disturbance is practically occurred in several engineering applications, especially in data storage systems. However, recently addressed controls for such problem were mostly dealt with its deterministic nature, which is rarely practical in real-time implementation. We present an adaptive control approach for DC motor systems with periodic stochastic disturbance whose frequency and magnitude are both random variables. We establish adaptive state feedback control which is linearly composed of nominal and corrective control parameter matrices. The former is derived from a nominal system model voiding disturbance and the latter is constructed from a disturbed system model by using Lyapunov stability theory. We carry out computer simulation to evaluate the proposed control methodology and compare to the recently addressed control method to demonstrate its superiority.