Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

基于归一化水体指数及其阈值自适应算法的水体遥感反演效果分析

归一化水体指数（NDWI）是水体遥感反演的一种重要指标，其阈值及修正直接影响反演结果的精度。基于Landsat-8与GF-1光学多光谱影像，使用归一化水体指数法阈值0（TH₀）、最大类间方差法（OTSU）自适应阈值（TH_otsu）与均值漂移聚类算法（Mean-Shift）自适应阈值（TH_MS）分别对典型正常水体、云雾覆盖水体、富营养化水体、高含泥沙水体进行水体遥感提取与效果分析，结果表明：正常水体以TH₀为阈值提取精度最高，THMS提取精度次之，TH_otsu提取精度最差；而云雾覆盖水体、富营养化水体以及含泥沙水体使用THMS提取精度最高，尤其少量云雾覆盖下的水体，TH_MS具有更明显的优势，TH₀提取精度次之，TH_otsu提取精度最差；对于不同的阈值，Land?sat-8比GF-1总体表现出更高的水体提取精度。Mean-Shift算法应用于NDWI阈值修正与水体遥感反演具有快速、水质适应性强、效果稳定的优势，对尤其是复杂条件下的水体信息遥感反演具有较好的提取效果。     

Interactive NBI and (E)NNC methods for the progressive exploration of the criteria space in multi-objective optimization and optimal control

A wide range of problems arising from real world applications present multiple and conflicting objectives to be simultaneously optimized. However, this multi-objective nature is too often neglected. Multi-objective optimization proved to be a powerful tool to correctly describe the trade-offs among conflicting objectives in a set of optimal solutions known as the Pareto set. This paper introduces an interactive method to solve multi-objective problems based on geometric considerations. The method returns a wider Pareto set, at a negligible computational cost, when compared to existing methods. The interactivity also allows the decision-maker to explore only relevant parts of the Pareto set. The extreme solutions yield insightful considerations on the generation of the scalarization parameters for the Normal Boundary Intersection and the Enhanced Normalized Normal Constraints methods. The proposed method is applied to: (i) three scalar multi-objective problems and (ii) the multi-objective optimal control of a tubular and a fed-batch reactor.     

一种应用于图像去噪的广义回归神经网络模型设计研究

为了更好地去除图像中的椒盐噪声、保留图像细节信息,提出了一种广义回归神经网络模型,适用于图像去噪。首先,对传统广义回归神经网络的原理进行了分析,并对采用的广义回归神经网络进行具体设计。然后对广义回归神经网络中的唯一可调参数（平滑因子）进行了优化。采用归一化均方误差和峰值信噪比指标进行具体算法性能分析。仿真试验结果显示：相比径向基神经网络和传统广义回归神经网络,提出算法的去噪能力更强,具有较高的峰值信噪比和较低的归一化均方误差,验证了提出算法的有效性和先进性。     

The power average operator for unbalanced linguistic term sets

In order to aggregate linguistic values of unbalanced linguistic term sets, this paper introduces the linguistic proportional 2-tuple power average operator, which can reflect the relationship among the aggregated values by considering the support for each value from others. Its advantage regarding other linguistic power average operators enables it to be used in such cases in which the linguistic term sets are not necessarily to be balanced, and the membership functions of the linguistic terms are utilized in the computational processes. In this operator, a linguistic proportional 2-tuple is represented by a normalized numerical representation. Some properties of the operator are discussed. A group decision making model based on the proposed operator is introduced. Finally an illustrative example is presented.     

Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition

Much of the existing work on action recognition combines simple features with complex classifiers or models to represent an action. Parameters of such models usually do not have any physical meaning nor do they provide any qualitative insight relating the action to the actual motion of the body or its parts. In this paper, we propose a new representation of human actions called sequence of the most informative joints (SMIJ), which is extremely easy to interpret. At each time instant, we automatically select a few skeletal joints that are deemed to be the most informative for performing the current action based on highly interpretable measures such as the mean or variance of joint angle trajectories. We then represent the action as a sequence of these most informative joints. Experiments on multiple databases show that the SMIJ representation is discriminative for human action recognition and performs better than several state-of-the-art algorithms.     

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.     

End-to-end scene text recognition using tree-structured models

Detecting and recognizing text in natural images are quite challenging and have received much attention from the computer vision community in recent years. In this paper, we propose a robust end-to-end scene text recognition method, which utilizes tree-structured character models and normalized pictorial structured word models. For each category of characters, we build a part-based tree-structured model (TSM) so as to make use of the character-specific structure information as well as the local appearance information. The TSM could detect each part of the character and recognize the unique structure as well, seamlessly combining character detection and recognition together. As the TSMs could accurately detect characters from complex background, for text localization, we apply TSMs for all the characters on the coarse text detection regions to eliminate the false positives and search the possible missing characters as well. While for word recognition, we propose a normalized pictorial structure (PS) framework to deal with the bias caused by words of different lengths. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms state-of-the-art methods both for text localization and word recognition.     

Gradient orientation pattern matching with the Hamming distance

This paper presents a novel pattern matching technique that is robust to illumination changes and the occlusion problem. The technique is based on the matching of gradient orientations in place of traditional image features such as intensities or gradients. Gradient orientations depend on the texture in an image. They are known to be insensitive to changes of image intensities that are often caused by time-varying illuminations or the auto-gain control (AGC) function of the camera. Moreover,the proposed method employs a voting strategy in the process of matching gradient orientations. The method works remarkably well even when a large part of the pattern is occluded with a foreign object. Consequently, the proposed method is robust to both irregular lighting conditions and the occlusion problem.     

An experimental intercomparison of gas meter calibrations

This paper presents four calibrations carried out in four different, independent, metrological accredited laboratories, on six diaphragm gas meters for domestic use (G4). The aim of this study is to evaluate the degree of metrological agreement among different calibration results, by means of the assessment of suitable factors (compatibility index, also known as normalized error). This application study is quite interesting in the field of “legal metrology”, when often conformity assessment are requested in order to assure the adequate behavior of a domestic gas meter. The six gas meters were calibrated in four different laboratories, each of them characterized by different values of the calibration uncertainty (also called CMC = Calibration and Measurement Capability, or BMC = Best Measurement Capability, or Minimum Uncertainty). Two alternative approaches about the metrological compatibility are introduced: a quantitative approach and a qualitative approach. The calibration results show that for diaphragm gas meters for domestic use, the metrological agreement should be preferable by means of the qualitative approach.