Grand Rapids, MI, United States
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.

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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.

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.

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.

Wei C.-C.,Digital Designs
Journal of Geophysical Research: Atmospheres | Year: 2014

In this study, a practical typhoon effective rainfall nowcasting (TERN) model was developed for use in real-time forecasting. The TERN model was derived from a data-driven adaptive network-based fuzzy inference system (ANFIS). The model inputs include meteorological data and radar reflectivity data. The model simulation process begins with an online typhoon warning issued by the Central Weather Bureau (CWB) of Taiwan. It is then determined whether the typhoon approaches the study area according to the typhoon track predicted by the CWB. When a typhoon hits Taiwan, various data are received from sensor instruments, including the ground precipitation data, typhoon climatological data, and radar reflectivity factor by using Weather Surveillance Radar, 1988, Doppler (WSR-88D) products. The study site was Shihmen Catchment. A maximum of 10 typhoon events from 2000 to 2010 were collected. Regarding the model construction, the input combinations of the ground precipitations and reflectivity factors over the catchment functioned as optimal input variables. To verify the practicability of the ANFIS-based TERN model, Typhoon Krosa, which hit Taiwan in 2007, was simulated. The results demonstrated that the proposed methodology of real-time rainfall forecasts during typhoon warning periods yielded favorable performance levels, reliably predicting results regarding 1h to 6h forecasting horizons. Key Points Typhoon effective rainfall nowcasting model was proposed for online forecasts Radar reflectivity was from the Wufenshan Radar Station by using WSR-88D A favorable and reliable predictions of 1-6 h forecasting horizons were provided ©2014. American Geophysical Union. All Rights Reserved.

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.

This paper proposes Bayesian networks (BNs) that combine polarization corrected temperature (PCT) and scattering index (SI) methods to identify rainfall intensity. To learn BN network structures, meta-heuristic techniques including tabu search (TS), simulated annealing (SA) and genetic algorithm (GA) were empirically evaluated and compared for efficiency. The proposed models were applied to the Tanshui river basin in Taiwan. The meteorological data from the Special Sensor Microwave/Imager (SSM/I) of the National Oceanic and Atmospheric Administration (NOAA) comprises seven passive microwave brightness temperatures, and was used to detect rain rates. The data consisted of 71 typhoons affecting the watershed during 2000-2012. A preliminary analysis using simple meta-heuristic BNs identified the main attributes, namely the brightness temperatures of 19, 22, 37 and 85. GHz for rainfall retrieval. Based on the preliminary analysis of a simple BN run, the advanced BNs combined with SI and PCT successfully demonstrated improved rain rate retrieval accuracy. To compare the proposed meta-heuristic BNs, the traditional SI method, the SI-based support vector regression model (SI-SVR), and artificial neural network (ANN) were used as benchmarks. The results showed that (1) meta-heuristic BN techniques can be used to identify the vital attributes of the rainfall retrieval problem and their causal relationships and (2) according to a comparison of BNs combined with PCT and SI and artificial intelligence (AI)-based models (SI-SVR and ANN), in heavy, torrential, and pouring rainfall, models of BNs combined with PCT and SI provide a superior retrieval performance than that of AI-based models. Therefore, this study confirms that meta-heuristic BNs combined with PCT and SI is an efficient tool for addressing rainfall retrieval problems. © 2014 Elsevier B.V.

Tropical cyclones often affect the western North Pacific region. Between May and October annually, enormous flood damage is frequently caused by typhoons in Taiwan. This study adopted machine learning techniques to forecast the hourly wind speeds over offshore islands near Taiwan during tropical cyclones. To develop a highly reliable surface wind speed prediction technique, the four kernel-based support vector machines for regression (SVR) models, comprising radial basis function, linear, polynomial, and Pearson VII universal kernels were used. To ensure the accuracy of the SVR model, traditional regressions and the parametric wind representations, comprising the modified Rankine profile, Holland wind profile, and DeMaria wind profile were used to compare wind speed forecasts. The methodology was applied to two islands near Taiwan, Lanyu, and Pengjia Islets. The forecasting horizon ranged from 1 to 6 h. The results indicated that the Pearson VII SVR is the most precise of the kernel-based SVR models, regressions, and parametric wind representations. Additionally, Typhoons Nanmadol and Saola which made landfall over Taiwan during 2011 and 2012 were simulated and examined. The results showed that the Pearson VII SVR yielded more favorable results than did the regressions and Holland wind profile. In addition, we observed that Holland wind profile seems applicable to open ocean but unsuitable for areas affected by topographic effects, such as the Central Mountain Range of Taiwan. ©2015. American Geophysical Union. All Rights Reserved.

Digital Designs | Date: 2014-04-17

Emergency notification system comprised of a data processor and a user input device for connecting the data processor to an emergency response entity through a communication network and allowing audio and data communication between the processor and the entity.

Digital Designs | Date: 2012-10-30

Personal security alarms.

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