The regional impacts of climate change on coastal environments and the aquaculture of Japanese scallops in northeast Asia: case studies from Dalian,China, and Funka Bay,Japan

Climate changes affect coastal environments and aquaculture, threatening food security and economic growth. Japanese scallop (Mizuhopecten yessoensis) culture is economically important for the coastal communities of Dalian, China, and Funka Bay, Japan. In this study, we combined satellite remote-sensing data, in situ observations, and a suitable aquaculture site selection model to explore the interactions between marine environments and climate variability over a recent 10-year period (2003–2012). Our selection of appropriate zones in these two Far Eastern regions and our analyses of climatic event (Arctic Oscillation (AO), winter East Asian monsoon (EAM), and El Niño/La Niña Southern Oscillation (ENSO)) and meteorological (precipitation, temperature, and wind) data allowed us to determine the impacts of climate change on regional coastal environments and prospects for scallop aquaculture. These analyses showed that AO and EAM strongly influenced the aquaculture areas on the Dalian coast through their effects on temperature during winter. We also determined that wind was the main driving force behind regional environmental changes during spring. Conversely, ocean conditions and suitable areas in Funka Bay changed rapidly relative to oceanic and atmospheric circulation. In Funka Bay, areas appropriate for scallop aquaculture and variations in chlorophyll-a concentration (which reflect the availability of algal food for scallops) were strongly correlated with ENSO, precipitation, and air temperature. These correlations demonstrate the influence of oceanic and atmospheric parameters on the productivity of scallop aquaculture in Funka Bay. Adaptation to oceanic and atmospheric changes should be considered when developing plans and management strategies for coastal scallop aquaculture in northeast Asia.     

Multi-modal Information Integration for Interactive Multi-agent Systems

This paper proposes a multi-agent system that carries out cooperative work. To achieve this, we use Fuzzy Associative Memory Organizing Unit Systems (FAMOUS) and Conceptual Fuzzy Sets (CFS). By using these proposed methods, each agent robot can decide its own behaviour to suit the situation in its environment. We apply this system to an Intelligent Transportation System (ITS) and do simulations.     

Cost-Efficient Traffic Aggregation Employing Cross-Layer Shared Protection

Shared protection/restoration is a promising solution for reducing protection resources and is supported at each layer of the current multi-layer networks. Software-defined networking is expected to reduce equipment cost as well as operational cost by orchestrating these shared protection functionalities. However, although protection resource sharing improves link utilization, it sometimes increases the required equipment. Meanwhile, traffic re-aggregation at each layer is an important technique for low volume traffic to utilize the underlying link capacity more efficiently, but re-aggregation also makes it difficult to share protection resources with traffic at lower layers. In this paper, we present multi-layer network design strategy and method that reduce equipment cost by means of both traffic re-aggregation at each layer and protection resource sharing among multiple service traffic at different layers. The strategy first prioritizes traffic re-aggregation at each layer, and then maximally delegates shared protection to lower layers as long as it does not increase the required capacity at the lower layer. Evaluation results from the example three-layer networks confirm that the proposed method can effectively reduce equipment cost compared to the conventional design method. Cost reduction is achieved by leveraging shared protection functions at multiple layers.     

k-NN classification of handwritten characters via accelerated GAT correlation

This paper addresses the problem of reinforcing the ability of the k-NN classification of handwritten characters via distortion-tolerant template matching techniques with a limited quantity of data. We compare three kinds of matching techniques: the conventional simple correlation, the tangent distance, and the global affine transformation (GAT) correlation. Although the k-NN classification method is straightforward and powerful, it consumes a lot of time. Therefore, to reduce the computational cost of matching in k-NN classification, we propose accelerating the GAT correlation method by reformulating its computational model and adopting efficient lookup tables. Recognition experiments performed on the IPTP CDROM1B handwritten numerical database show that the matching techniques of the simple correlation, the tangent distance, and the accelerated GAT correlation achieved recognition rates of 97.07%, 97.50%, and 98.70%, respectively. The computation time ratios of the tangent distance and the accelerated GAT correlation to the simple correlation are 26.3 and 36.5 to 1.0, respectively.     

Efficient blind dereverberation and echo cancellation based on independent component analysis for actual acoustic signals

This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT??= 670 ms reverberant environment.     

Exponential ARX model-based long-range predictive control strategy for power plants

For nonlinear thermal power plants whose dynamics vary with load demand, a load-dependent exponential ARX (Exp-ARX) model, which can effectively describe the nonlinear properties of the plants, is presented. The Exp-ARX model requires only off-line identification. Based on the model, a constrained multivariable generalized predictive control (CMGPC) strategy is designed and implemented in a simulation of 375 MW thermal power plants. This CMGPC algorithm does not resort to on-line parameter estimation and can more exactly predict the future outputs of the nonlinear plants, so it shows better reliability and control performance than the usual GPC algorithm.