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Grand Rapids, MI, United States

Digital Designs, or DD Audio is an American manufacturer of high-end consumer audio products. They produce home, and mobile audio products, serving both the sound quality, and sound pressure categories of the mobile audio market, and has won more SPL competitions than any other company. Domestic-made products is a large aspect of Digital Designs, as nearly all of their products are still handmade in the United States. Wikipedia.

Wei C.-C.,Digital Designs
Environmental Modelling and Software | Year: 2015

This study developed a methodology for formulating water level models to forecast river stages during typhoons, comparing various models by using lazy and eager learning approaches. Two lazy learning models were introduced: the locally weighted regression (LWR) and the k-nearest neighbor ( kNN) models. Their efficacy was compared with that of three eager learning models, namely, the artificial neural network (ANN), support vector regression (SVR), and linear regression (REG). These models were employed to analyze the Tanshui River Basin in Taiwan. The data collected comprised 50 historical typhoon events and relevant hourly hydrological data from the river basin during 1996-2007. The forecasting horizon ranged from 1h to 4h. Various statistical measures were calculated, including the correlation coefficient, mean absolute error, and root mean square error. Moreover, significance, computation efficiency, and Akaike information criterion were evaluated. The results indicated that (a) among the eager learning models, ANN and SVR yielded more favorable results than REG (based on statistical analyses and significance tests). Although ANN, SVR, and REG were categorized as eager learning models, their predictive abilities varied according to various global learning optimizers. (b) Regarding the lazy learning models, LWR performed more favorably than kNN. Although LWR and kNN were categorized as lazy learning models, their predictive abilities were based on diverse local learning optimizers. (c) A comparison of eager and lazy learning models indicated that neither were effective or yielded favorable results, because the distinct approximators of models that can be categorized as either eager or lazy learning models caused the performance to be dependent on individual models. © 2014 Elsevier Ltd. Source

Wei C.-C.,Digital Designs | Hsu N.-S.,National Taiwan University | Huang C.-L.,National Taiwan University
Water Resources Management | Year: 2014

This study proposes a two-stage intelligence-based pumping control (TWOPC) model for real-time pumping operation to solve the complex problem of estimating the desired pump flow and determining the optimal combination of pumps deployed in a flood event. In Stage I of the model, the desired pump flow was forecasted using the multilayer perceptron (MLP) with hydrological information including rainfall and basin runoffs, forebay water levels, and pump flows. In Stage II, the optimal pump combination was forecasted using tree-derived rules obtained from C4.5, classification and regression tree (CART), and chi-squared automatic interaction detection (CHAID) classifiers. The East Chung-Kong pumping station in New Taipei City was used as the study area. The pumping facilities included both submersible and upright axial pumps. The optimal input-output patterns, derived from a deterministic pumping operation optimization model, were used to train and validate the proposed TWOPC model. Data for this study were collected from three storms and four typhoons that affected an urban drainage basin. A total of 1,765 records were available. The results indicated that the case with a lag time of 5 min provided the most desirable pump flows in Stage I, and the C4.5 tree-based classifier performed well in Stage II. In addition, Typhoons Sinlaku (2) (2008/9/15) and Jangmi (2008/9/29) were selected for simulating the TWOPC model. The results demonstrated that the TWOPC model provided a more favorable performance than the traditional experienced method did. Overall, the proposed two-stage prediction model successfully addressed the problems of both determining the desired pump flow and optimal pump combination. © 2013 Springer Science+Business Media Dordrecht. Source

Hsu N.-S.,National Taiwan University | Huang C.-L.,National Taiwan University | Wei C.-C.,Digital Designs
Journal of Hydrology | Year: 2015

This study applies an Adaptive Network-based Fuzzy Inference System (ANFIS) and a Real-Time Recurrent Learning Neural Network (RTRLNN) with an optimized reservoir release hydrograph using Mixed Integer Linear Programming (MILP) from historical typhoon events to develop a multi-phase intelligent real-time reservoir operation model for flood control. The flood control process is divided into three stages: (1) before flood (Stage I); (2) before peak flow (Stage II); and (3) after peak flow (Stage III). The models are then constructed with either three phase modules (ANFIS-3P and RTRLNN-3P) or two phase (Stage I. +. II and Stage III) modules (ANFIS-2P and RTRLNN-2P). The multi-phase modules are developed with consideration of the difference in operational decision mechanisms, decision information, release functions, and targets between each flood control stage to solve the problem of time-consuming computation and difficult system integration of MILP. In addition, the model inputs include the coupled short lead time and total reservoir inflow forecast information that are developed using radar- and satellite-based meteorological monitoring techniques, forecasted typhoon tracks, meteorological image similarity analysis, ANFIS and RTRLNN. This study uses the Tseng-Wen Reservoir basin as the study area, and the model results showed that RTRLNN outperformed ANFIS in the simulated outcomes from the optimized hydrographs. This study also applies the models to Typhoons Kalmaegi and Morakot to compare the simulations to historical operations. From the operation results, the RTRLNN-3P model is better than RTRLNN-2P and historical operations. Further, because the RTRLNN-3P model combines the innovative multi-phase module with monitored and forecasted decision information, the operation can simultaneously, effectively and automatically achieve the dual goals of flood detention at peak flow periods and water supply at the end of a typhoon event. © 2014 Elsevier B.V. Source

The purposes of this study were to forecast the hourly typhoon wind velocity over the Penghu Islands, and to discuss the effects of the terrain of the Central Mountain Range (CMR) of Taiwan over the Penghu Islands based on typhoon tracks. On average, a destructive typhoon hits the Penghu Islands every 15-20 yr. As a typhoon approaches the Penghu Islands, its track and intensity are influenced by theCMR topography. Therefore, CMR complicates the wind forecast of the Penghu Islands. Six main typhoon tracks (classes I-VI) are classified based on typhoon directions, as follows: (I) the direction of direct westward movement across the CMR of Taiwan, (II) the direction of northward movement along the eastern coast of Taiwan, (III) the direction of northward movement traveling through Taiwan Strait, (IV) the direction of westward movement traveling through Luzon Strait, (V) the direction of westward movement traveling through the southern East China Sea (near northern Taiwan), and (VI) the irregular track direction. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLPNN) were used as the forecasting technique for predicting the wind velocity. A total of 49 typhoons from 2000 to 2012 were analyzed. Results showed that the ANFIS models provided high-reliability predictions for wind velocity, and the ANFIS achieved more favorable performance than did the MLPNN. In addition, a detailed discussion on the interaction of the CMR with the Penghu Islands based on various track directions is provided. For class I, the CMR is observed to have significantly influenced variations in wind speed when typhoons approached the Penghu Islands. In addition, the winds on the Penghu Islands were observed to have been influenced by the distance from the typhoon center to the Penghu Islands for all classes except class II. © 2014 American Meteorological Society. Source

Hsu N.-S.,National Taiwan University | Huang C.-L.,National Taiwan University | Wei C.-C.,Digital Designs
Journal of Hydrologic Engineering | Year: 2015

This study develops an original methodology for forecasting real-time reservoir inflow hydrographs during typhoons, taking advantage of meteoro-hydrological methods such as analysis of typhoon hydrographs, numerical typhoon track forecasts, statistic typhoon central impulse-based quantitative precipitation forecasts model based on a real-time revised approach (TCI-RTQPF), real-time recurrent learning neural network (RTRLNN), and adaptive network-based fuzzy inference system (ANFIS). To derive the inflow hydrograph induced by interaction between typhoon rain bands, terrain, and monsoons, the inventive novel ensemble numerical-statistic impulse techniques are employed. The inflow during peak flow, inflection, and direct runoff ending (DRE) periods (impulse signal) are used for the deriving process. The hydrograph analysis is used to examine the mechanism between typhoon center location, wind field, precipitation, and the inflow hydrograph, and to establish the evaluation methods. Additionally, a novel total inflow forecast model is developed using image hashing and ANFIS for selecting optimal derived hydrograph. The experiment is conducted in the Tseng-Wen Reservoir basin, Taiwan. Results demonstrate that the wind field-based and moving dynamics-based approach of typhoon can effectively evaluate the time of peak flow, inflection point, and DRE incorporating terrain and monsoon effects. The effective functions for deriving impulse signal include blended polynomial, exponential, and power functions, and for deriving inflow hydrograph, multinomial Gaussian functions. Finally, the real-time experimental outcomes show that the proposed innovative practical methodology can accurately forecast the real-time reservoir inflow hydrograph that the average error of Typhoon Krosa is 7.81% within 32 h average forecasted lead time, and Typhoon Morakot, 9.78% within 79 h forecasted lead time. © 2015 American Society of Civil Engineers. Source

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