Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers’ first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.

Distributed control, as a potential solution to decreasing communication demands in microgrids, has drawn much attention in recent years. Advantages of distributed control have been extensively discussed, while its impacts on microgrid performance and stability, especially in the case of communication latency, have not been explicitly studied or fully understood yet. This paper addresses this gap by proposing a generalized theoretical framework for small-signal stability analysis and performance evaluation for microgrids using distributed control. The proposed framework synthesizes generator and load frequency-domain characteristics, primary and secondary control loops, as well as the communication latency into a frequency-domain representation which is further evaluated by the generalized Nyquist theorem. In addition, various parameters and their impacts on microgrid dynamic performance are investigated and summarized into guidelines to help better design the system. Case studies demonstrate the effectiveness of the proposed approach.

A network of 23 weather stations was used to detect existing oases in Southern California. Four stations, separated from one another by 15 -25 miles (24 -40 km), were closely examined. Data were strongly affected by the distance of the stations from the Pacific Ocean. This and other city-scale effects made the network inadequate for detection of urban oases. We also conducted traverse measurements of temperature and humidity in the Whittier Narrows Recreation Area in Los Angeies County on Sept. 8-10, 1993. Near-surface air temperatures over vegetated areas were 1 -2°C lower than background air temperatures. We estimate that vegetation may lower urban temperatures by 1°C, while the establishment of vegetative canopies may lower local temperatures by an additional 2°C. An increase in vegetation in residential neighborhoods may reduce peak loads in the Los Angeles area by 0.3 GW, and reduce energy consumption by 0.2 BkWh/year, saving $20 million annually. Large additional savings would result from regional cooling. t This workwassupportedby theCaliforniaInstitutefor EnergyEfficiency(CW_.E) throughtheU. S. Department of Energy,undercontractDE-AC0376SF00098. _:Presentaffiliation:CapitalEnvironmental Eng. Corp.,Taipei, Taiwan,R.O.C.

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A simple yet very effective slew rate enhancement (SRE) method is proposed. The proposed SRE feedback is off for small signal operation to preserve an amplifier's small signal performance, and is activated to enhance slew rate (SR) and linearity only when an amplifier is slewing. Compared with a conventional OTA, the OTA with the proposed SRE scheme shows SRE by a factor of 23.2, THD improvement of 6dB and preserved small signal performance with only 1.2% area and 2% power consumption overhead. Compared with the adaptive biasing method, the proposed scheme shows 300% SR improvement, 18dB THD improvement but with power and area consumption decreased by 11.1% and 25% respectively Keywords-slew rate enhancement I.

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The purpose of this paper is to discuss how to solve the problem of on-line identification of model parameters of Li-ion battery and on-line estimation of SOC. Based on the matlab/simulink platform, a first-order RC equivalent circuit model of the battery is built, and a joint estimation algorithm of the model parameters and SOC of the lithium ion battery is designed based on the dynamic model, which is compared with the single adaptive Kalman filter algorithm (AEKF). The simulation results show that the proposed joint estimation algorithm can make effective online estimation and update of the battery model parameters and SOC. The average estimation error of SOC is less than 2.8%, the estimation accuracy is higher than that of adaptive Kalman filter, and its robustness level is relatively high.