Search

The most apparent practical advantage of del Nido cardioplegia (DNC) is that it allows the surgeon a longer arrest period before a subsequent dose is needed, as opposed to the conventional St. Thomas' cardioplegia solution where the recommended ischemic time is ∼15-20 minutes. In this study, we explored the incidence of arrhythmia after cross-clamp removal as a surrogate for a safe myocardial ischemic time with the use of DNC in adult heart surgery. A total of 113 patients who had undergone heart valve repair and/or replacement surgery at the National University Hospital, Singapore, were investigated. This single-center retrospective study was conducted on a population where DNC had been used for myocardial protection between January 2017 and April 2019. Cardioplegia ischemic time interval groups were not significant predictors of postoperative arrhythmia, defibrillation, and intraoperative intra-aortic balloon pump usage. Crude comparison of postoperative outcomes showed no significant differences in any other postoperative variables, including mortality and total hospital stay. From the results of the present study, it appears that there is no optimal ischemic time interval for the administration of DNC within a 120-minute time period. It is likely that DNC has a redosing interval of, and may provide adequate myocardial protection, for up to 120 minutes.

Advancements in digitalisation and cyber-physical automation are opening up new frontiers in the marine industry such as autonomous shipping and smart manufacturing of ships. However, the progress in automating the ship design process which is still very human dependent, is slow. This paper introduces an automated ship design and optimisation concept by Hybridising Evolutionary Algorithm and Morphing (HEAM) to enable a more intelligent and efficient ship design process. It maps the entire ship hull form into a genotype using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and flexible modification through morphing to create efficient designs for the hull form. This new method achieves predictive designs with a performance improvement of 11.67%, while achieving increased efficiency and minimum user dependency.

This paper presents a novel Direction of Arrival (DOA) estimation technique called Cross Cumulant-MUSIC (CC-MUSIC) which jointly employs higher order cumulant statistics and the root-MUSIC algorithm to perform high-resolution DOA estimation in low Signal-to-Noise Ratio (SNR) scenarios. From the simulation results based out of a 4 element uniform linear array and a far-field narrowband signal source, CC-MUSIC outperforms second-order DOA estimation techniques such as root-MUSIC and ESPRIT with a minimum average of10.99% to 46.33% depending on the snapshot values at SNR of

Powder-binder separation during injection moulding causes defects such as cracking, warpage or anisotropic shrinkage during firing. In this paper, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) have been previously used to analyse powder distribution within the green body for metal injection moulding are used for ceramics. TGA and DSC are used to characterize the mass loss and heat of fusion of the binder system, silicon nitride feedstock and test-bars. TGA can measure the volume fraction of powder in green parts directly with 1.76 vol% difference from nominal volume fraction and variations up to 0.177 vol%. The DSC empirical model predicted volume fraction of powder in green parts with 1.76 vol% difference from nominal volume fraction and variations up to 1.710 vol%. Using rule of mixture, it over predicted volume fraction of powder in green parts by 6.78 vol% from nominal volume fraction and with variations up to 2.510 vol%.

This paper presents a preliminary study of a novel polynomial-solving Direction of Arrival (DOA) estimator called Root-Transformation Matrix (root-T) technique which includes an investigation of its performance against current DOA algorithms such as root-MUSIC and improved-MUSIC. The main objective of this work is to conserve the performance of improved-MUSIC which achieves high DOA estimation accuracy and resolution while reducing the cost of computational complexity. It's shown that the Root-T performs better in low SNR with performance improvement of 86.7% and with closely-spaced signal condition with performance improvement of 96.8% as compared to root-MUSIC without degrading improved-MUSIC performance while reducing mean computational time by 49.5%.

The reliability of a wireless sensor network (WSN) is often assessed on node-to-node communication performance through link characterization. Long-term routing stability is an aspect of a WSN that is often overlooked in routing protocol implementations. In this paper, we investigate the routing stability of ZigBee PRO implemented WSN nodes that are deployed in a real-world environment. Frequent changes in next hops along routing paths between source and destination nodes can result in an increase in undesired energy consumption of the WSN. Hence, the relative routing path usage count, usage rate of unique next hop and switching frequency count are proposed as routing stability indicators. Our findings show that routing stability is subjected to not only the quality of a link but also to the implemented routing protocols, deployed environment and routing options available. More importantly, next hops with low usage rates are shown to experience a higher probability of disconnection from the Neighbor Table of respective source nodes, causing them to be short-lived. The need to avoid these links shows the importance of evaluating routing stability and identifying network bottlenecks.

As the marine industry moves towards the industry 4.0 era, the role of automated smart design is becoming increasingly significant. This offers an ability to produce highly customisable design and to integrate with the product-lifecycle process such as digitalised ship production and ship operations to in an efficient process. Currently, the hull form optimisation process is performed manually using ‘trial-and-error’ approach, which is not efficient. Focusing on automated smart design, this paper introduces a hybrid evolutionary algorithm and morphing (HEAM). It works by mapping the entire hull form (phenotype) into a chromosome (genotype), which allows global shape modification using a novel 2D morphing method. By combining this 2D morphing and Genetic Algorithm (GA), it enables optimal hull designs to be produced more rapidly with no user intervention.

Amongst non-conventional generators, photovoltaics (PVs) are becoming more popular owing to their relatively low costs and convenience. However, the intermittency of the PV generator outputs require accurate forecasting, planning, and optimal management. Existing forecasting methods, which are based on either clear-box or black-box modelling, have room for improvement, especially in the accuracy of capturing the underlying PV characteristics and forecasting of PV yields. This paper explores the use of a priori knowledge of PV systems to heuristically improve their clear-box and black-box models. The paper then further explores the use of heuristic grey-box modelling to identify uncertain parameters of physical principle. Incorporating black boxes to account for such un-modelled uncertainties inherent in a clear box provides the resultant grey box with improved forecasting performance and offers a new approach to practical system modelling. The experimental results on an installed PV system have co...

There exists various ways of modeling and forecasting photo-voltaic (PV) systems. These methods can be categorized, in board-way, under either definite equations models (white or clear-box) or heuristic data-driven artificial intelligence models (black-box). The two directions of modeling pose a number of drawbacks. To benefit from both worlds, this paper proposes a novel method where clear-box model is extended to a grey-box model by modeling uncertainities using focused time-delay neural network models. The grey-box or semi-definite model was shown to exhibit enhanced forecasting capabilities.

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future.

This paper presents a generalized hybrid evolutionary optimization structure that not only combines both nondeterministic and deterministic algorithms on their individual merits and distinct advantages, but also offers behaviors of the three originating classes of evolutionary algorithms (EAs). In addition, a robust mutation operator is developed in place of the necessity of mutation adaptation, based on the mutation properties of binary-coded individuals in a genetic algorithm. The behaviour of this mutation operator is examined in full and its performance is compared with adaptive mutations. The results show that the new mutation operator outperforms adaptive mutation operators while reducing complications of extra adaptive parameters in an EA representation.

Wireless Sensor Network's communication reliability is greatly influenced by the spatial related network challenges found in the physical space between communicating nodes. In this paper, ZigBee based sensor nodes are experimented under the influence of two distinct spatial related network challenges (i) poor deployed environment and (ii) human movements. WSN parameters obtained are used to develop an ANFIS based model designed to predict these spatial related network challenges. Using ANFIS model prediction accuracies as performance indices, WSN parameters are analysed from the physical and network layers perspective. Physical layer's link properties, reception strength and reception variability, are shown to be key indicators to spatial related network challenges. The parameters observed are Mean RSSI, Average Coefficient of Variation RSSI, Neighbour Table Connectivity and Bi-directional Neighbour Table Connectivity.

In this paper, Adaptive Neuro-Fuzzy Interference System (ANFIS) technique is used to develop models to predict two conditions commonly found in a Wireless Sensor Network's deployment; these conditions are failure due to (i) poorly deployed environment and (ii) human movements. ANFIS models are trained using parameters obtained from actual ZigBee PRO nodes' Neighbour Table experimented under the influence of associated network challenges. These parameters are Mean RSSI, Standard Deviation RSSI, Average Coefficient of Variation RSSI and Neighbour Table Connectivity. The individual and combined effects of parameters are investigated in-depth. Results showed the mean RSSI is a critical parameter and the combination of mean RSSI, ACV RSSI and NTC produced the best prediction results (∼92%) for all ANFIS models.

As the world becomes more connected and customer’s ‘needs and wants’ lean more towards bespoke products, the way they are designed and manufactured must adapt. Recognised as the main driver for the future of the manufacturing value chain, Industry 4.0 (i4) is rapidly gaining momentum worldwide. One of the key enablers of the i4 design model to achieve mass customisation manufactured at a mass production cost is the computational intelligence based Computer-Automated Design (CAutoD). This paper demonstrates how CAutoD realises the i4 concept for smart design of future ships and smart ships through-life. Following an overview of the i4 and CAutoD interface, a smart ship design technique is introduced to form an automated closed-loop approach to the entire ship design process. Then, key challenges and future directions on this roadmap are discussed. Lastly, a framework in which the concepts of morphing and free-form deformation are embedded into an evolutionary algorithm is\ud developed to automate the design and optimisation process of the\ud hull form.

Following the first three industrial revolutions, Industry 4.0 (I4) aims at realizing mass customization at a mass production cost. Currently, however, there is a lack of smart analytics tools for achieving such a goal. This paper investigates this issues and then develops a predictive analytics framework integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a self-organizing map (SOM) is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The selection of patterns from big data with SOM helps with clustering and with the selection of optimal attributes. A car customization case study shows that the SOM is able to assign new clusters when growing knowledge of customer needs and wants. The self-organizing tool offers a number of features suitable to smart design that is required in realizing Industry 4.0.

Industry 4.0 aims at achieving mass customization at a\ud mass production cost. A key component to realizing this is accurate\ud prediction of customer needs and wants, which is however a\ud challenging issue due to the lack of smart analytics tools. This\ud paper investigates this issue in depth and then develops a predictive\ud analytic framework for integrating cloud computing, big data\ud analysis, business informatics, communication technologies, and\ud digital industrial production systems. Computational intelligence\ud in the form of a cluster k-means approach is used to manage\ud relevant big data for feeding potential customer needs and wants\ud to smart designs for targeted productivity and customized mass\ud production. The identification of patterns from big data is achieved\ud with cluster k-means and with the selection of optimal attributes\ud using genetic algorithms. A car customization case study shows\ud how it may be applied and where to assign new clusters with\ud growing knowledge of customer needs and wants. This approach\ud offer a number of features suitable to smart design in realizing\ud Industry 4.0.