• 제목/요약/키워드: hybrid systems

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Signal Processing and Development Process Based on "MOOC + SPOC + Flipped Classroom"

  • Bei Qiao;Yan Mi
    • Journal of Information Processing Systems
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    • v.20 no.1
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    • pp.105-115
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    • 2024
  • The hybrid teaching approach of "MOOC + SPOC + Flipped Classroom" overcomes the constraints of time and space that are typically associated with traditional teaching methods, thus compensating for the shortcomings of traditional approaches. These changes in education are driven by the "Internet+" wave and the growing popularity of online teaching. The "MOOC + SPOC + Flipped Classroom" hybrid teaching mode can successfully compensate for the drawbacks of traditional teaching methods, thereby overcoming their restrictions. By defining relevant concepts, one can distill the key characteristics of the "MOOC + SPOC + Flipped Classroom" hybrid teaching mode. Formative assessment was employed to thoroughly evaluate the effectiveness of this teaching approach. By leveraging the advantages of massive open online course (MOOC), small private online course (SPOC), and flipped classroom, the "MOOC +SPOC + Flipped Classroom" teaching mode incorporates real-time student assessment through peer evaluation, computer-aided evaluation, and teacher evaluation. This mode promotes the simultaneous development of theoretical knowledge and practical skills, helping students to establish strong foundations while fostering their practical abilities. While the traditional teaching method remains fruitful, the convenience of today's network allows the teaching profession to continually evolve. The traditional teaching mode heavily relies on teachers, making it impossible to conduct lessons without them. However, the development of MOOC enables students to seek knowledge online from their preferred teachers, rather than solely relying on their assigned instructors.

Bridge Simulation System with Soil-Foundation-Structure Interaction (지반 구조 상호작용을 고려한 교량 시뮬레이션 시스템)

  • Kim, Ik-Hwan;Han, Bong-Koo
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.12 no.4
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    • pp.168-178
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    • 2008
  • The hybrid simulation test method is a versatile technique for evaluating the seismic performance of structures by seamlessly integrating both physical and numerical simulations of substructures into a single test mode. In this paper, a software framework that integrates computational and experimental simulation has been developed to simulate and test a bridge structural system under earthquake loading. Using hybrid simulation, the seismic response of complex bridge structural systems partitioned into multiple large-scale experimental and computational substructures at networked distributed experimental and computational facilities can be evaluated. In this paper, the examples of application are presented in terms of a bridge model with soil-foundation-structure interaction.

Comparison and Implementation of Optimal Time Series Prediction Systems Using Machine Learning (머신러닝 기반 시계열 예측 시스템 비교 및 최적 예측 시스템 구현)

  • Yong Hee Han;Bangwon Ko
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.17 no.4
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    • pp.183-189
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    • 2024
  • In order to effectively predict time series data, this study proposed a hybrid prediction model that decomposes the data into trend, seasonality, and residual components using Seasonal-Trend Decomposition on Loess, and then applies ARIMA to the trend component, Fourier Series Regression to the seasonality component, and XGBoost to the remaining components. In addition, performance comparison experiments including ARIMA, XGBoost, LSTM, EMD-ARIMA, and CEEMDAN-LSTM models were conducted to evaluate the prediction performance of each model. The experimental results show that the proposed hybrid model outperforms the existing single models with the best performance indicator values in MAPE(3.8%), MAAPE(3.5%), and RMSE(0.35) metrics.

A New Item Recommendation Procedure Using Preference Boundary

  • Kim, Hyea-Kyeong;Jang, Moon-Kyoung;Kim, Jae-Kyeong;Cho, Yoon-Ho
    • Asia pacific journal of information systems
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    • v.20 no.1
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    • pp.81-99
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    • 2010
  • Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ratio of the hit set size to the recommended set size. The hit set size means the number of success of recommendations in our experiment, and the test set size means the number of purchased items during the test period. Experimental test result shows the hit ratio of BC and NC is bigger than that of TC. This means using neighbors Is more effective to recommend new items. That is hybrid algorithm using CF is more effective when recommending to consumers new items than the algorithm using only CB. The reason of the smaller hit ratio of BC than that of NC is that BC is defined as a dummy or virtual customer who purchased all items of target customers' and neighbors'. That is centroid of BC often shifts from that of TC, so it tends to reflect skewed characters of target customer. So the recommendation algorithm using NC shows the best hit ratio, because NC has sufficient information about target customers and their neighbors without damaging the information about the target customers.

Operation and performability analysis of modular cells (모듈러 셀의 운영과 수행성 해석)

  • Heo, Gyeon;Jang, Seok-Ho;Jung, Hyun-Ho;Lee, Sang-Moon;Woo, Gwang-Bang;Kim, Hak-Bae
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.1263-1266
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    • 1997
  • In a fault-tolerant modern manufacturing systms characterized by the configuration, in which automated redundant machines prone to unexpected failures are interconnected with other complex subsystems such as AGV's, robots, computer control systems to produce complete parts, faulures together with repairs and reconfigurations should be considered as the three basic events to be modeled for computing the performance of manufacturing systems. In this papre, transient analysis is applied to modular cell manufacturing systems form a performability viewpoint whose modeling adantage is that various performanc e measures can be evaluated compositely in the context of application. The hypothertical modular cells are modeled firstly with hybrid decomposition method and availability measures as special cases of performability are computed and comments on performabililty modeling analysis are mentioned.

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ONIOM and Its Applications to Material Chemistry and Catalyses

  • Morokuma, Keiji
    • Bulletin of the Korean Chemical Society
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    • v.24 no.6
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    • pp.797-801
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    • 2003
  • One of the largest challenges for quantum chemistry today is to obtain accurate results for large complex molecular systems, and a variety of approaches have been proposed recently toward this goal. We have developed the ONIOM method, an onion skin-like multi-level method, combining different levels of quantum chemical methods as well as molecular mechanics method. We have been applying the method to many different large systems, including thermochemistry, homogeneous catalysis, stereoselectivity in organic synthesis, solution chemistry, fullerenes and nanochemistry, and biomolecular systems. The method has recently been combined with the polarizable continuum model (ONIOM-PCM), and was also extended for molecular dynamics simulation of solution (ONIOM-XS). In the present article the recent progress in various applications of ONIOM and other electronic structure methods to problems of homogeneous catalyses and nanochemistry is reviewed. Topics include 1. bond energies in large molecular systems, 2. organometallic reactions and homogeneous catalysis, 3. structure, reactivity and bond energies of large organic molecules including fullerenes and nanotubes, and 4. biomolecular structure and enzymatic reaction mechanisms.

Fuzzy Cognitive Maps built in NI LabVIEW for control of dynamic process (NI LabVIEW를 이용한 동적 제어용 FCM 제어기)

  • Balashov, Vadim S.;Skatova, Darya D.;Choe, Seong-Ju;Jo, Hyeon-Chan
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2007.04a
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    • pp.217-220
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    • 2007
  • This paper studies method of controlling dynamic process with Fuzzy Cognitive Map (FCM) built in NI LabVIEW software. FCM is the hybrid methodology that combines fuzzy logic and neural networks. A FCM will be developed using NI LabVIEW software to model and control a process of dynamic system. Nowadays more autonomous and intelligent systems are very useful in many areas of people lives especially related with Complex Systems.

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Influence Evaluation of Electric Vehicle Load on Distribution Systems by the penetration rate of Electric Vehicle (전기자동차 보급 전망에 따른 배전계통에서의 영향 평가)

  • Kim, Chul-Woo;Han, Seung-Ho;Song, Taek-Ho;Jeong, Moon-Gyu
    • Proceedings of the KIEE Conference
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    • 2011.07a
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    • pp.256-257
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    • 2011
  • The development for Eco-friendly cars has been expanded as the concern about environmental pollution and a rise in gas prices. The Electric Vehicle(EV) and Plug in Hybrid Electric Vehicle(PHEV) are generally connected on distribution power systems to charge the traction batteries. The growing number of EV/PHEVs could have a effect on distribution power systems and result in overload of power utilities and power quality problems. In order to reduce the adverse effect on distribution power systems, the influence of electric vehicle loads should be evaluated. In this paper, the influence of electric vehicle loads is evaluated by using OpenDSS(Open Source Distribution System Simulator) according to the penetration rate of electric vehicle.

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Type-2 Fuzzy Logic Optimum PV/inverter Sizing Ratio for Grid-connected PV Systems: Application to Selected Algerian Locations

  • Makhloufi, S.;Abdessemed, R.
    • Journal of Electrical Engineering and Technology
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    • v.6 no.6
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    • pp.731-741
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    • 2011
  • Conventional methodologies (empirical, analytical, numerical, hybrid, etc.) for sizing photovoltaic (PV) systems cannot be used when the relevant meteorological data are not available. To overcome this situation, modern methods based on artificial intelligence techniques have been developed for sizing the PV systems. In the present study, the optimum PV/inverter sizing ratio for grid-connected PV systems with orientation due south and inclination angles of $45^{\circ}$ and $60^{\circ}$ in selected Algerian locations was determined in terms of total system output using type-2 fuzzy logic. Because measured data for the locations chosen were not available, a year of synthetic hourly meteorological data for each location generated by the PVSYST software was used in the simulation.

Adaptive Control of a Class of Nonlinear Systems Using Multiple Parameter Models

  • Lee Choon-Young
    • International Journal of Control, Automation, and Systems
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    • v.4 no.4
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    • pp.428-437
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    • 2006
  • Many physical systems are hybrid in the sense that they have continuous behaviors and discrete phenomena. In control system with multiple models, switching strategy and stability of the closed-loop system under switching are very important issues. In this paper, a novel adaptive control scheme based on multiple parameter models is proposed to cope with a change in Parameters. Switching strategy guarantees the non-increase in the global control Lyapunov function if the estimation of Lyapunov function value converges. Least-square estimation is used to find the estimated value of the Lyapunov function. Switching and adaptation law guarantees the stability of closed-loop system in the sense of Lyapunov. Simulation results on anti-lock brake system are shown to verify the effectiveness of the proposed controller in view of a large change in system parameters.