Luminance behaviour of PLEDs with integration of nanomaterials

Incorporation of nanomaterials in device structure is the key to enhance performance of polymer light emitting diodes (PLEDs). The major challenges that impede competence of PLEDs, for application in display technology, are (i) non-availability of stable low work function metals to act as cathode, (ii) presence of charge trapping centers in the polymer chains and (iii) total internal reflection of light at ITO/glass and glass/air interfaces. The foremost problem leads to increase in turn ON voltage of the device and reduction in electron injection from cathode. Low injection and high trapping probability of electrons lead to charge imbalance in the emissive layer and shifting of recombination zone towards cathode. This immensely constrains the formation and radiative decay of excitons in the emissive layer and declines the luminosity of the device. In this review, experimental studies on the integration of nanomaterials in PLED structures to enhance device luminance are presented. The diverse impact of their geometric features, ionization potential, electrical conductivity and refractive index on the carrier transport and light extraction in PLEDs is discussed and a perspective on this evolving research path is provided.     

Counterfactual inference to predict causal knowledge graph for relational transfer learning by assimilating expert knowledge --Relational feature transfer learning algorithm

Transfer learning (TL) is a machine learning (ML) method in which knowledge is transferred from the existing models of related problems to the model for solving the problem at hand. Relational TL enables the ML models to transfer the relationship networks from one domain to another. However, it has two critical issues. One is determining the proper way of extracting and expressing relationships among data features in the source domain such that the relationships can be transferred to the target domain. The other is how to do the transfer procedure. Knowledge graphs (KGs) are knowledge bases that use data and logic to graph-structured information; they are helpful tools for dealing with the first issue. The proposed relational feature transfer learning algorithm (RF-TL) embodies an extended structural equation modelling (SEM) as a method for constructing KGs. Additionally, in fields such as medicine, economics, and law related to people’s lives and property safety and security, the knowledge of domain experts is a gold standard. This paper introduces the causal analysis and counterfactual inference in the TL domain that directs the transfer procedure. Different from traditional feature-based TL algorithms like transfer component analysis (TCA) and CORelation Alignment (CORAL), RF-TL not only considers relations between feature items but also utilizes causality knowledge, enabling it to perform well in practical cases. The algorithm was tested on two different healthcare-related datasets — sleep apnea questionnaire study data and COVID-19 case data on ICU admission — and compared its performance with TCA and CORAL. The experimental results show that RF-TL can generate better transferred models that give more accurate predictions with fewer input features.     

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.     

A three-stage multi-objective heterogeneous integrated model with decomposition-reconstruction mechanism and adaptive segmentation error correction method for ship motion multi-step prediction

Reliable and accurate ship motion prediction is essential for ship navigation at sea and marine operations. Although previous studies have yielded rich results in the field of ship motion prediction, most of them have ignored the importance of the dynamic characteristics of ship motion for constructing forecasting models. Besides, the limitations of the single model and the autocorrelation characteristics of the residual series are also unfavorable factors that hinder the forecasting performance. To fill these gaps, a multi-objective heterogeneous integration model based on decomposition-reconstruction mechanism and adaptive segmentation error correction method is proposed in this paper for ship motion multi-step prediction. Specifically, the proposed model is divided into three stages, which are decomposition-reconstruction mechanism, multi-objective heterogeneous integration model and adaptive segmentation error correction method. The effectiveness of the proposed model is verified using four sets of real ship motion data collected from two sites in the South China Sea. The evaluation results show that the proposed model can effectively improve the prediction performance and outperforms other traditional models and state-of-the-art models in the field of ship motion prediction. Prospectively, the model proposed in this study can be used as an effective aid to ship warning systems and has the potential for practical application in ship marine operations.     

Development of acoustic denoising learning network for communication enhancement in construction sites

This paper presents a novel denoising approach based on deep learning and signal processing to improve communication efficiency. Construction activities take place when different trades come to the site for overlapped periods to perform their works, which may easily produce hazardous noise levels. The existence of noise affects workers' health issues, especially hearing and rhythm of the heart, and impacts communication efficiency between workers. The proposed approach employs signal processing technique to transform the noisy audio into image and utilize neural networks to extract noisy features and denoise the image. The denoised image is then converted to obtain the denoised audio. Experiments on reducing the side effect of several common noises in construction sites were conducted, compared with the performance of denoising using conventional wavelet transform. Standard objective measures, such as signal-to-noise ratio (SNR), and subjective measures, such as listening tests are used for evaluations. Our experimental results show that the proposed algorithm achieved significant improvements over the traditional method, as evidenced by the following quantitative results of median value: MSE of 0.002, RMSE of 0.049, SNR of 5.7 dB, PSNR of 25.8 dB, and SSR of 8.Results indicate that the proposed algorithm outperforms conventional denoising methods in terms of both objective and subjective evaluation metrics and have the potential to facilitate communication between site workers when facing different noise sources inevitably.     

Conceptual design evaluation considering the ambiguity semantic variables fusion with conflict beliefs: An integrated Dempster-Shafer evidence theory and intuitionistic fuzzy -VIKOR

Quantifying the uncertain linguistic evaluation from decision-makers (DMs) is one of the most challenging parts in the conceptual design decision. Although fuzzy decision models have been widely used to capture potential uncertainty by assigning a fuzzy term with the certain belief, the ambiguity subjective evaluation of semantic variables with conflict beliefs derived from DMs have not been well addressed. To solve this drawback, a concept decision model based on Dempster-Shafer (DS) evidence theory and intuitionistic fuzzy -Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) considering the ambiguity semantic variables fusion is proposed. Firstly, by incorporating semantic variables of intuitionistic fuzzy sets (IFSs), the diversified semantic judgments and its belief will be taken into account to form an ambiguity semantic initial decision matrix; secondly, the DS combination rule will be used to fuse the different semantic variables of multi-DMs in each scheme, update the belief of each semantic variable, and then the semantic fusion value matrix of the scheme will be constructed; finally, the weight of each evaluation objective will be calculated based on the value matrix and information entropy model, IFS-VIKOR model will be constructed to rank the concepts. A case study of the tree climbing and trimming machine will be employed to verify the proposed decision model. This decision model considering diversifying semantic variables and the conflict belief is proven to be effective compared with the IFS-SAW and ISF-TOPSIS.     

An IIoT-driven and AI-enabled framework for smart manufacturing system based on three-terminal collaborative platform

Smart manufacturing has great potential in the development of network collaboration, mass personalised customisation, sustainability and flexibility. Customised production can better meet the dynamic user needs, and network collaboration can significantly improve production efficiency. Industrial internet of things (IIoT) and artificial intelligence (AI) have penetrated the manufacturing environment, improving production efficiency and facilitating customised and collaborative production. However, these technologies are isolated and dispersed in the applications of machine design and manufacturing processes. It is a challenge to integrate AI and IIoT technologies based on the platform, to develop autonomous connect manufacturing machines (ACMMs), matching with smart manufacturing and to facilitate the smart manufacturing services (SMSs) from the overall product life cycle. This paper firstly proposes a three-terminal collaborative platform (TTCP) consisting of cloud servers, embedded controllers and mobile terminals to integrate AI and IIoT technologies for the ACMM design. Then, based on the ACMMs, a framework for SMS to generate more IIoT-driven and AI-enabled services is presented. Finally, as an illustrative case, a more autonomous engraving machine and a smart manufacturing scenario are designed through the above-mentioned method. This case implements basic engraving functions along with AI-enabled automatic detection of broken tool service for collaborative production, remote human-machine interface service for customised production and network collaboration, and energy consumption analysis service for production optimisation. The systematic method proposed can provide some inspirations for the manufacturing industry to generate SMSs and facilitate the optimisation production and customised and collaborative production.     

The use of PCA and signal processing techniques for processing time-based construction settlement data of road embankments

Instrumentation is beneficial in civil engineering for monitoring structures during their construction and operation. The data collected can be used to observe real-time response and develop data-driven models for predicting future behaviour. However, a limited number of sensors are usually used for on-site civil engineering construction due to cost restrictions and practicalities. This results in relatively small raw datasets, which often contain errors and anomalies. Interpreting and making judicious use of the available dataset for developing reliable predictive model represents a significant challenge. Therefore, it is essential to pre-process and clean the data for improving their quality. To date, little investigation has been performed in the application of such data cleaning methods to geotechnical engineering datasets collected from full-scale sites. The purpose of this study is to apply simple and effective data pre-processing techniques to site-data collected from a highway embankment constructed on a sequence of soil layers of different physical make-up and non-linear consolidation characteristics. Various cleaning methods were applied to magnetic extensometer data collected for monitoring settlement within foundation soils beneath the embankment. PCA was used to explore raw data, identify and remove outliers. Numerous filtering and smoothing methods were used to clean noise in the data and their results were further compared using RMSE and NMSE. The methods adopted for data pre-processing and cleaning proved very effective for capturing the raw settlement behaviour on site. The findings from this study would be useful to site engineers regarding complex decision-making relating to ground response due to embankment construction. This also has positive prospects for developing dynamic prediction models for embankment settlement.     

Production logistics digital twins: Research profiling,application, challenges and opportunities

In the era of Industry 4.0, Production Logistic Digital Twins (PLDTs) have garnered remarkable attention from both academic and industrial communities. This is evident from the growing number of research publications on PLDTs in international scientific journals and conferences. However, given the diversity and complexity of production logistics activities, there is a pressing need for systematic literature review to chart past research and identify potential directions for future endeavors. Therefore, this study primarily focuses on the application of Digital Twins (DTs) in Production Logistics (PL). Firstly, an analysis of PLDTs research profiling is carried out based on general trends, keywords, application scenarios, and basic functions. Secondly, the functional characteristics of PLDTs are examined while summarizing their advantages and limitations across various application scenarios such as transportation, packaging, warehousing, material distribution, and information processing. And the roles played by smart technologies such as Internet of Things (IoT) in PLDTs system are discussed. Finally, possible challenges and future directions of PLDTs in industrial application are presented, accompanied by appropriate classification and extensive recommendations.     

Risk assessment of China-Pakistan Fiber Optic Project (CPFOP) in the light of Multi-Criteria Decision Making (MCDM)

The China-Pakistan Economic Corridor (CPEC) is considered as an excellent breakthrough for improving the economic and security situation in the region. The estimated worth of CPEC is 62$ billion which is comprising of 49 developmental projects. China-Pakistan Fiber Optic Project (CPFOP) is one of the core projects among these, which will deliver safe route of voice traffic between both countries. CPFOP is greatly beneficial in terms of enhanced security and revenue generation. Currently, Pakistan’s international connectivity is via submarine cables. CPFOP will provide an alternative route for international telecom traffic and also assist in achieving the rapidly growing internet traffic demand in Pakistan. It is estimated that 17 million people will get benefit from this project. However, every project has some undesirable impacts. The aim of this research paper is twofold; 1st to trace out the pros and cons of CPFOP. 2ndly, performing a risk assessment of CPFOP by using Fuzzy VIKOR technique. This approach will help in prioritizing a list of failure modes of Fiber Optic Cable (FOC). Lastly, this paper will help authorities for optimizing and safeguarding national interest in the wake of CPFOP.     

A review of digital twin in product design and development

In the era of digitalization, there are many emerging technologies, such as the Internet of Things (IoT), Digital Twin (DT), Cloud Computing and Artificial Intelligence (AI), which are quickly developped and used in product design and development. Among those technologies, DT is one promising technology which has been widely used in different industries, especially manufacturing, to monitor the performance, optimize the progresses, simulate the results and predict the potential errors. DT also plays various roles within the whole product lifecycle from design, manufacturing, delivery, use and end-of-life. With the growing demands of individualized products and implementation of Industry 4.0, DT can provide an effective solution for future product design, development and innovation. This paper aims to figure out the current states of DT research focusing on product design and development through summarizing typical industrial cases. Challenges and potential applications of DT in product design and development are also discussed to inspire future studies.     

An enhanced 3D model and generative adversarial network for automated generation of horizontal building mask images and cloudless aerial photographs

Information extracted from aerial photographs is widely used in the fields of urban planning and design. An effective method for detecting buildings in aerial photographs is to use deep learning to understand the current state of a target region. However, the building mask images used to train the deep learning model must be manually generated in many cases. To overcome this challenge, a method has been proposed for automatically generating mask images by using textured three-dimensional (3D) virtual models with aerial photographs. Some aerial photographs include clouds, which degrade image quality. These clouds can be removed by using a generative adversarial network (GAN), which leads to improvements in training quality. Therefore, the objective of this research was to propose a method for automatically generating building mask images by using 3D virtual models with textured aerial photographs. In this study, using GAN to remove clouds in aerial photographs improved training quality. A model trained on datasets generated by the proposed method was able to detect buildings in aerial photographs with IoU = 0.651.     

Assessing future landscapes using enhanced mixed reality with semantic segmentation by deep learning

Architecture, engineering, and construction projects need to be promoted in harmony with the natural environment and with the aim of preserving people’s living environment. At the planning and design stage, decision-makers and stakeholders share and assess landscape images during and after construction in order to avoid as much uncertainty as possible when performing environmental impact assessment. Given the lack of a standard visualization method for future landscapes that do not yet exist, mixed reality (MR), which overlays virtual content onto a real scene, has attracted attention in the field of landscape design. One challenge in MR is occlusion, which occurs when virtual objects obscure physical objects that should be rendered in the foreground. In MR-based landscape visualization, the distance between the MR camera and real objects located in front of the virtual objects might vary and might be large, causing difficulty for existing occlusion handling methods. In the process of landscape design, an evidence-based approach has also become important. Landscape index estimation using semantic segmentation by deep learning, which can recognize the surrounding environment, has been actively studied for landscape assessment. In this study, semantic segmentation by deep learning was integrated into an MR system to enable dynamic occlusion handling and landscape index estimation for both existing and designed landscape assessment. This system can be operated on a mobile device with video communication over the internet by connecting to real-time semantic segmentation on a high-performance personal computer. The applicability of the developed system is demonstrated through accuracy verification and case studies.     

An immersive virtual reality serious game to enhance earthquake behavioral responses and post-earthquake evacuation preparedness in buildings

Enhancing the earthquake behavioral responses and post-earthquake evacuation preparedness of building occupants is beneficial to increasing their chances of survival and reducing casualties after the mainshock of an earthquake. Traditionally, training approaches such as seminars, posters, videos or drills are applied to enhance preparedness. However, they are not highly engaging and have limited sensory capabilities to mimic life-threatening scenarios for the purpose of training potential participants. Immersive Virtual Reality (IVR) and Serious Games (SG) as innovative digital technologies can be used to create training tools to overcome these limitations. In this study, we propose an IVR SG-based training system to improve earthquake behavioral responses and post-earthquake evacuation preparedness. Auckland City Hospital was chosen as a case study to test our IVR SG training system. A set of training objectives based on best evacuation practice has been identified and embedded into several training scenarios of the IVR SG. Hospital staff (healthcare and administrative professionals) and visitors were recruited as participants to be exposed to these training scenarios. Participants’ preparedness has been measured along two dimensions: 1) Knowledge about best evacuation practice; 2) Self-efficacy in dealing with earthquake emergencies. Assessment results showed that there was a significant knowledge and self-efficacy increase after the training. In addition, participants acknowledged that it was easy, helpful, and engaging to learn best evacuation practice knowledge through the IVR SG training system.     

Poly(vinyl alcohol)-based photosensitive water-borne coating for low-temperature color-filter production

A photosensitive water-borne overcoat comprising poly(vinyl alcohol), a glycoluril crosslinker, and a water-soluble photoacid generator was developed. The passivation coating has two features: low-temperature processability and applicability to organic-solvent-susceptible films. Photo-exposure and subsequent baking at 85 °C and development with water produced PGMEA-insoluble and transparent overcoat patterns. Uncured color patterns that were susceptible to the PGMEA-based coating solution remained intact after water-based overcoat application. By exploiting the features of the passivation coating, color patterns of green, red, and white were produced onto a glass substrate at a process temperature of 85 °C.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.     

Palette designer: A color-code design tool

The US Federal Aviation Administration (FAA) has developed a standard set of colors for coding information on air traffic control (ATC) displays. A significant complication was that the air traffic controller population includes people who have color-vision deficiencies (CVDs). We wrote a software tool to assist the FAA in selecting a preliminary color set. It accepts a set of luminances and chromaticity coordinates as input and: (1) Draws graphics and calculates color-related figures of merit to predict whether the set will be acceptable for color-normal and CVD users; (2) Flags colors and pairings that violate human factors criteria; and (3) Allows designers to adjust the colors and see the resulting changes immediately. The tool has been used to perform a pilot study for the FAA’s color-set development project and should be useful for designing other color-coding sets, also.     

Energy consumption mode identification and monitoring method of process industry system under unstable working conditions

Process industry systems under unstable working conditions are prone to potential anomalies, deviating from the original transition trajectory, and taking longer than expected to return to stability due to persistent disturbances from uncertainties and experience-based regulation errors. The energy waste caused by this situation has not received sufficient attention, and cannot be addressed by existing energy consumption monitoring methods. Herein, an energy consumption mode (ECM) identification and monitoring method under unstable working conditions is proposed, consisting of ECM identification model and multi-mode dynamic monitoring model, focusing on the variation rules of the correlation between energy consumption and other states of the system. In the ECM identification stage, the ECM correlation parameters that reflect the comprehensive production information are selected. Then, given the transfer characteristics of ECM, a Hidden Semi-Markov Model (HSMM) is constructed to fit the migration between modes and the duration within modes. The Variational Bayesian Gaussian Mixture Model is introduced to improve the HSMM, which solves the problem of lacking prior knowledge of ECM and achieves the automatic classification and online identification of ECM. In the dynamic monitoring stage of multi-ECMs, a series of dynamic kernel principle component analysis models are established, and the corresponding monitoring thresholds are set for each ECM. By calculating the maximum of the posteriori probability and the mode thresholds, the ECMs under unstable conditions can be accurately identified and automatically monitored. Compared with previous methods, the proposed method reduces the false detection rate and missed detection rate of abnormal ECM identification to 1.04% and 1.31% in the actual slag grinding production process, which proves its effectiveness.     

A cooperative game theory based user-centered medical device design decision approach under uncertainty

The medical device conceptual design decision-making is a process of coordinating pertinent stakeholders, which will significantly affect the quality of follow-up market competitiveness. However, as the most challenging parts of user-centered design, traditional methods are mainly focusing on determining the priorities of the evaluation criteria and forming the comprehensive value (utility) of the conceptual scheme, may not fully deal with the interaction and interdependent between the conflicts of interest among stakeholders and weigh the ambiguous influence on the overall design expectations, which results in the unstable decision-making results. To overcome this drawback, this paper proposes a cooperative game theory based decision model for device conceptual scheme under uncertainty. The proposed approach consists of three parts: first part is to collect and classify needs of end users and professional users based on predefined evaluation criteria; second part is using rough set theory technique to create criteria correlation diagram and scheme value matrix from users; and third part is developing the fuzzy coalition utility model to maximize the overall desirability through the criteria correlation diagram with the conflict of interests of end and professional users considered, and then selecting the optimal scheme. A case study of blood pressure meter is used to illustrate the proposed approach and the result shows that this approach is more robust compared with the widely used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach.