Port performance in container transport logistics: A multi-stakeholder perspective

This study proposes a measurement instrument for port performance in the context of container transport logistics (CTLs) by taking perspectives from different port stakeholders. An importance-performance analysis (IPA) is used to develop an analytical tool for investigating the importance and performance (IP) of major container ports in South Korea against individual CTLs criterion. The main originality of this study is the development of a measurement instrument to provide managerial and operational insights to both port managers (i.e. terminal operating companies) and policy makers (i.e. port authorities and government) for stakeholder management in CTLs. The analysis helps port managers and policy makers to converge the different objectives and concerns for better management

Development models and policies for maritime clusters

Cluster research has been a key subject in regional studies over the past two decades. In waterborne transport geography, maritime clusters are centres of industry and economic development. This research investigates the corresponding mechanisms, originating from diverse modes of regional economic development, for maritime cluster evolution. More importantly, development models and policies which are suitable for each mode of maritime cluster development are proposed – namely the maritime production cluster, maritime service cluster and the all-in-one maritime cluster. Empirical case studies are presented for each mode of maritime cluster. As a whole, this chapter provides practical strategic recommendations for policy makers on how maritime clusters should be developed with a systematic plan and mapping, en route to higher value-added maritime clusters

Improving Quality of Service in Baseband Speech Communication

Speech is the most important communication modality for human interaction. Automatic speech recognition and speech synthesis have extended further the relevance of speech to man-machine interaction. Environment noise and various distortions, such as reverberation and speech processing artifacts, reduce the mutual information between the message modulated inthe clean speech and the message decoded from the observed signal. This degrades intelligibility and perceived quality, which are the two attributes associated with quality of service. An estimate of the state of these attributes provides important diagnostic information about the communication equipment and the environment. When the adverse effects occur at the presentation side, an objective measure of intelligibility facilitates speech signal modification for improved communication. The contributions of this thesis come from non-intrusive quality assessment and intelligibility-enhancing modification of speech. On the part of quality, the focus is on predictor design for limited training data. Paper A proposes a quality assessment model for bounded-support ratings that learns efficiently from a limited amount of training data, scales easily with the sampling frequency, and provides a platform for modeling variations in the individual subjective ratings. The predictive performance of the model for the mean of the subjective quality ratings compares favorably to the state-of-art in the field. Patterns in the spread of the individual ratings are captured in the feature space of the training data. Paper B focuses on enhancing predictive performance for the mean of the quality variable when the signal feature space is sparsely sampled by the training data. Using a Gaussian Processes framework, the deterministic signal-based feature set is augmented with a stochastic feature that is hypothesized to be jointly distributed with the target quality rating. An uncertainty propagation mechanism ensures that the variance of this feature is reflected in the prediction. The proposed architecture can take advantage of i) data that cannot be pooled due to subjective test protocol incompatibility and ii) models trained on data that are no longer available. With respect to intelligibility enhancement, a hierarchical perspective of the speech communication process, extended from foundational work in the field, is used in paper C to create a unified framework for method analysis and comparison. A high-level intelligibility measure related to the probability for correct recognition is derived using a hit-or-miss distortion criterion in the transcription domain. The measure is used to optimize two speech modifications at different levels of the message encoding hierarchy leading to significantly enhanced intelligibility in noise. The conceptual novelty of the method comes at the cost of higher complexity and the requirement for additional information including message transcription, sound segmentation, and a model of speech. Mapping the high-level measure to a lower level takes away the need for additional information and preserves asymptotically high-level optimality. Two methods are proposed to reduce degradation in the accuracy of the spectral dynamics due to additive noise. The focus of paper D is dynamics preservation in a range that is lower-bounded by an optimal band-power threshold. The performance of the method is competitive but allows for improvement in power efficiency. This issue is addressed in paper E which proposes and optimizes a distortion measure for spectral dynamics leading to a significant increase in intelligibility. Use of functional optimization techniques allows for families of solutions, among which are dynamic range compressors adaptive to the statistics of the speech and the noise.QC 20140523</p

Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery

Owing to the space–air–ground integrated networks (SAGIN), seaborne shipping has attracted increasing interest in the research on the motion behavior knowledge extraction and navigation pattern mining problems in the era of maritime big data for improving maritime traffic safety management. This study aims to develop a novel unsupervised methodology for feature extraction and knowledge discovery based on automatic identification system (AIS) data, allowing for seamless knowledge transfer to support trajectory data mining. The unsupervised hierarchical methodology is constructed from three parts: trajectory compression, trajectory similarity measure, and trajectory clustering. In the first part, an adaptive Douglas–Peucker with speed (ADPS) algorithm is created to preserve critical features, obtain useful information, and simplify trajectory information. Then, dynamic time warping (DTW) is utilized to measure the similarity between trajectories as the critical indicator in trajectory clustering. Finally, the improved spectral clustering with mapping (ISCM) is presented to extract vessel traffic behavior characteristics and mine movement patterns for enhancing marine safety and situational awareness. Comprehensive experiments are conducted and implemented in the Chengshan Jiao Promontory in China to verify the feasibility and effectiveness of the novel methodology. Experimental results show that the proposed methodology can effectively compress the trajectories, determine the number of clusters in advance, guarantee the clustering accuracy, and extract useful navigation knowledge while significantly reducing the computational cost. The clustering results are further explored and follow the Gaussian mixture distribution, which can help provide new discriminant criteria for trajectory clustering

An integrated approach for port selection, ship scheduling and financial analysis

Liner shipping, Port selection, Ship scheduling, Financial analysis, Optimisation, Intelligent system,