A design method for an intelligent manufacturing and service system for rehabilitation assistive devices and special groups

The maturity of Industrial 4.0 technologies (smart wearable sensors, Internet of things [IoT], cloud computing, etc.) has facilitated the iteration and digitization of rehabilitation assistive devices (RADs) and the innovative development of intelligent manufacturing systems of RADs, expanding the value-added component of smart healthcare services. The intelligent manufacturing service mode, based on the concept of the product life cycle, completes the multi-source data production process analysis and the optimization of manufacturing, operation, and maintenance through intelligent industrial Internet of things and other means and improves the product life cycle management and operation mechanism. The smart product-service system (PSS) realizes the value-added of products by providing users with personalized products and value-added services, service efficiency, and sustainable development and gradually forms an Internet-product-service ecosystem. However, research on the PSS of RADs for special populations is relatively limited. Thus, this paper provides an overview of an IoT-based production model for RADs and a smart PSS-based development method of multimodal healthcare value-added services for special people. Taking the hand rehabilitation training devices for autistic children as a case, this paper verifies the effectiveness and availability of the proposed method. Compared with the traditional framework, the method used in this paper primarily helps evaluate rehabilitation efficacy, personalizes schemes for patients, provides auxiliary intelligent manufacturing service data and digital rehabilitation data for RAD manufacturers, and optimizes the product iteration development procedures by combining user-centered product interaction, multimodal evaluation, and value-added design. This study incorporates the iterative design of RADs into the process of smart PSS to provide some guidance to the RADs design manufacturers.     

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.     

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.     

Engineering knowledge formalization and proposition for informatics development towards a CAD-integrated DfX system for product design

Target design methodologies (DfX) were developed to cope with specific engineering design issues such as cost-effectiveness, manufacturability, assemblability, maintainability, among others. However, DfX methodologies are undergoing the lack of real integration with 3D CAD systems. Their principles are currently applied downstream of the 3D modelling by following the well-known rules available from the literature and engineers’ know-how (tacit internal knowledge).This paper provides a method to formalize complex DfX engineering knowledge into explicit knowledge that can be reused for Advanced Engineering Informatics to aid designers and engineers in developing mechanical products. This research work wants to define a general method (ontology) able to couple DfX design guidelines (engineering knowledge) with geometrical product features of a product 3D model (engineering parametric data). A common layer for all DfX methods (horizontal) and dedicated layers for each DfX method (vertical) allow creating the suitable ontology for the systematic collection of the DfX rules considering each target. Moreover, the proposed framework is the first step for developing (future work) a software tool to assist engineers and designers during product development (3D CAD modelling).A design for assembly (DfA) case study shows how to collect assembly rules in the given framework. It demonstrates the applicability of the CAD-integrated DfX system in the mechanical design of a jig-crane. Several benefits are recognized: (i) systematic collection of DfA rules for informatics development, (ii) identification of assembly issues in the product development process, and (iii) reduction of effort and time during the design review.     

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.     

Visually deficient working conditions and reduced work performance in office workers: Is it mediated by visual discomfort?

IntroductionThe main purpose of this cross-sectional study was to investigate whether visual discomfort acts as a mediating factor between perceived visual ergonomic working conditions and self-rated visual performance among office workers who carry out administrative tasks and computer-based work at the Swedish Tax Agency.MethodsA questionnaire was sent to 94 office workers addressing: 1) perceived visual quality of the visual display units; 2) prevalence of eye symptoms; and 3) self-rated visual performance. Eighty-six persons (54 women (63%), 31 men (36%), and 1 of unspecified sex) answered the questionnaire. Multiple regression analysis investigated the association between visual ergonomic working conditions and visual performance, both with and without visual discomfort as a mediator.ResultsThe group mean of the Indexed survey questions indicated a reasonably good quality of visual ergonomic working conditions, a relative absence of eye symptoms, and acceptable self-rated visual performance. Results from multiple regression analysis showed a significant association between perceived visual ergonomic working conditions and self-rated visual performance (r² = 0.30, β = 0.327, p < 0.01). When visual discomfort was used as a mediator, the association between perceived visual ergonomic working conditions and self-rated visual performance remained the same (r² = 0.32, β = 0.315, p < 0.01).DiscussionIt was remarkable to discover that self-rated visual performance was independent of visual discomfort. Possible explanations include exposure factors not included in the current study, such as dry air and sensory irritation in the eyes, psychosocial stress, time spent performing near work activities, or time exposed to visually deficient working conditions.Relevance to industryThe strong connection between satisfaction with visual ergonomic working conditions and productivity in this study has implications for workplace profitability and staff satisfaction. If productivity is enhanced by better visual ergonomic working conditions, then managers of workplaces may be able to improve work outcomes by optimizing the physical work environment.     

Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances

The application of machine learning (ML) techniques to metal-based nanomaterials has contributed greatly to understanding the interaction of nanoparticles, properties prediction, and new materials discovery. However, the prediction accuracy and efficiency of distinctive ML algorithms differ with different metal-based nanomaterials problems. This, alongside the high dimensionality and nonlinearity of available datasets in metal-based nanomaterials problems, makes it imperative to review recent advances in the implementation of ML techniques for these kinds of problems. In addition to understanding the applicability of different ML algorithms to various kinds of metal-based nanomaterials problems, it is hoped that this work will help facilitate understanding and promote interest in this emerging and less explored area of materials informatics. The scope of this review covers the introduction of metal-based nanomaterials, several techniques used in generating datasets for training ML models, feature engineering techniques used in nanomaterials-machine learning applications, and commonly applied ML algorithms. Then, we present the recent advances in ML applications to metal-based nanomaterials, with emphasis on the procedure and efficiency of algorithms used for such applications. In the concluding section, we identify the most common and efficient algorithms for distinctive property predictions. The common problems encountered in ML applications for metal-based nanoinformatics were mentioned. Finally, we propose suitable solutions and future outlooks for various challenges in metal-based nanoinformatics research.     

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.     

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.     

Non-invasive embedded system hardware/firmware anomaly detection based on the electric current signature

Quality control is a critical aspect of the modern electronic circuit industry. In addition to being a pre-requisite to proper functioning, circuit quality is closely related to safety, security, and economic issues. Quality control has been reached through system testing. Meanwhile, device miniaturization and multilayer Printed Circuit Boards have increased the electronic circuit test complexity considerably. Hence, traditional test processes based on manual inspections have become outdated and inefficient. More recently, the concept of Advanced Manufacturing or Industry 4.0 has enabled the manufacturing of customized products, tailored to the changing customers’ demands. This scenario points out additional requirements for electronic system testing: it demands a high degree of flexibility in production processes, short design and manufacturing cycles, and cost control. Thus, there is a demand for circuit testing systems that present effectiveness and accessibility without placing numerous test points. This work is focused on automated test solutions based on machine learning, which are becoming popular with advances in computational tools. We present a new testing approach that uses autoencoders to detect firmware or hardware anomalies based on the electric current signature. We built a test set-up using an embedded system development board to evaluate the proposed approach. We implemented six firmware versions that can run independently on the test board – one of them is considered anomaly-free. In order to obtain a reference frame to our results, two other classification techniques (a computer vision algorithm and a random forest classification model) were employed to detect anomalies on the same development board. The outcomes of the experiments demonstrated that the proposed test method is highly effective. For several test scenarios, the correct detection rate was above 99%. Test results showed that autoencoder and random forest approaches are effective. However, random forests require all data classes to be trained. Training an autoencoder, on the other hand, only requires the reference (anomaly-free) class.     

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.     

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.     

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.     

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.     

Member combination selection for product collaborative design under the open innovation model

A temporary product collaborative design team (PCDT) formed by customers and candidate service providers is the main organization form required to complete the task of product collaborative design (PCD) under the open innovation model. Therefore, the aim of this study was to implement synergy effect-based member combination selection (SE-MCS) while ensuring customer participation in the PCD. First, the conceptual framework of SE-MCS method was developed to characterise the SE-MCS process that includes the customer. Second, SE-MCS indicators were determined by analysing the characteristics of PCD under the open innovation model, and the quantitative calculation methods for these indicators were provided. Subsequently, the mathematical model for SE-MCS considering customer participation was established, and a multi-objective optimisation algorithm was adopted to identify the optimal scheme. Finally, the formation of a design team for a beach waste collection vehicle was performed to verify the proposed method. The results showed that the proposed method is more suitable to implement SE-MCS of PCD under the open innovation model. It can facilitate the smooth operation of PCD tasks and improve the quality and efficiency of teamwork, thereby increasing customer satisfaction.     

Effect of working position and cold environment on muscle activation level and fatigue in the upper limb during manual work tasks

Several occupational groups are exposed to periods of low ambient temperatures while performing manual work tasks outdoors. Work tasks typically include heavy lifting, tool handling, and overhead work. This study evaluated the effect of working position and cold environment on muscle activation level (%RMS_max) and fatigue in the upper limb during manual work tasks. Fourteen male participants (25 ± 3 years, 80.9 ± 6.4 kg, 182 ± 5 cm) completed a 2-h test protocol consisting of five test periods alternating with four work periods, wearing identical sets of clothing, under cold (−15 °C) and control (5 °C) conditions. The work periods consisted of manual work at the hip level, manual overhead work, and a lifting exercise. The test periods consisted of isometric maximal voluntary contractions (MVC) and seated rest. Skin temperatures decreased during cold exposure, especially in the extremities. %RMS_max in the forearm was higher in the cold condition both during overhead work and work at the hip level than that for the same work in the control condition, especially at the end of the test when the difference was approximately 25% (equating to 2–3 %RMS_max). For the middle deltoid muscle, the %RMS_max was approximately three times (or 10 %RMS_max) higher during overhead work than work at the hip level, but there was no additional cost of working in the cold. Signs of deltoid muscle fatigue (decrease in electromyography median power frequency and an increase in %RMS_max) were observed during the overhead work periods in both temperature conditions. No decrease in MVC, as a sign of overall muscle fatigue, was observed in either condition.Relevance to industryThis study demonstrated that when wearing suitable cold-weather protective clothing, the adverse effect of work posture is much higher than that of cold on muscle demand and physical strain.     

A parameter-extended case-based reasoning method based on a functional basis for automated experiential reasoning in mechanical product designs

The mechanical product design process involves much experiential reasoning which relies extensively on accumulated experience knowledge and ambiguous synthetic decision of experts (ASDE). This makes it hard to achieve the automated, intelligent and rapid design of mechanical products. Furthermore, due to the lack of consideration of experts' cognition of product functions and structures in the application of the current case-based reasoning (CBR) method in the field of automated experiential reasoning (AER), the parameter solving process is separated from ASDE. Aiming at improving the accuracy and intelligence level of AER in mechanical product design, this paper proposed a parameter-extended CBR (PECBR) method based on a functional basis by integrating ASDE into AER. The PECBR method mainly contains two parts: firstly, in order to acquire and quantitatively describe expert experiential knowledge to provide an effective basis for AER, a knowledge representation method integrating a function-flow-parameter matrix set (FFP-MS) using functional bases and a parameter experiential correlation matrix (PEC-M) extracted from FFP-MS were presented for mechanical products, where the FFP-MS characterized the operation of function and energy flow during the working process of products. An acquisition rule for FFP-MS was designed to extract the degree of correlation between each two parameters, in which the implicit knowledge hiding among functions, flows and parameters was mined to form PEC-M; secondly, to cope with the difficulty in integrating ASDE into AER, a feature-weighted case adaptation (FCA) method was proposed by adopting a presented weighted kernel support vector machine (WK-SVM) and dynamic particle swarm optimization (DPSO). The FCA method can achieve the intelligent and automated solving of product parameters through identifying PEC-M during the case adaptation process. Two case studies on two-stage reducers and corn huskers were carried out to demonstrate the validity of the PECBR method. Compared with other conventional CBR methods, PECBR method can derive a more accurate value of parameters in mechanical product designs especially in the case of limited similar cases.     

A digital twin-based multidisciplinary collaborative design approach for complex engineering product development

With the ever-increasing demand for personalized product functions, product structure becomes more and more complex. To design a complex engineering product, it involves mechanical, electrical, automation and other relevant fields, which requires a closer multidisciplinary collaborative design (MCD) and integration. However, the traditional design method lacks multidisciplinary coordination, which leads to interaction barriers between design stages and disconnection between product design and prototype manufacturing. To bridge the gap, a novel digital twin-enabled MCD approach is proposed. Firstly, the paper explores how to converge the MCD into the digital design process of complex engineering products in a cyber-physical system manner. The multidisciplinary collaborative design is divided into three parts: multidisciplinary knowledge collaboration, multidisciplinary collaborative modeling and multidisciplinary collaborative simulation, and the realization methods are proposed for each part. To be able to describe the complex product in a virtual environment, a systematic MCD framework based on the digital twin is further constructed. Integrate multidisciplinary collaboration into three stages: conceptual design, detailed design and virtual verification. The ability to verify and revise problems arising from multidisciplinary fusions in real-time minimizes the number of iterations and costs in the design process. Meanwhile, it provides a reference value for complex product design. Finally, a design case of an automatic cutting machine is conducted to reveal the feasibility and effectiveness of the proposed approach.     

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.     

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.