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The purpose of this paper is to propose a DC-port voltage balance strategy realizing it by logic combination modulation (LCM). This voltage balance strategy is brief and high efficient, which can be used in many power electronic devices adopting the cascaded H-bridge rectifier (CHBR) such as power electronic transformer (PET).

The CHBR is typically as a core component in the power electronic devices to implement the voltage or current conversion. The modulation method presented here is aiming to solve the voltage imbalance problem occurred in the CHBR with more stable work station and higher reliability in ordinary operating conditions. In particular, by changing the switch states smoothly and quickly, the DC-port voltage can be controlled as the ideal value even one of the modules in CHBR is facing the load-removed problem.

By using the voltage balance strategy of LCM, the problem of voltage imbalance occurring in three-phase cascaded rectifiers has been solved properly. With the lower modulation depth, the efficiency of the strategy is shown to be better and stronger. The strategy can work reliably and quickly no matter facing the problem as load-removed change or the ordinary operating conditions.

The limitation of the proposed DC-port voltage balance strategy is calculated and proved, in a three-module CHBR, the LCM could balance the DC-port voltage while one module facing the load-removed situation under 0.83 modulation depth.

This paper provides a useful and particular voltage balance strategy which can be used in the topology of three-phase cascaded rectifier. The value of the strategy is that a brief and reliable voltage balance method in the power electronic devices can be achieved. What is more, facing the problem, such as load-removed, in outport, the strategy can response quickly with no switch jump and switch frequency rising.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

Regular cable trench inspection is crucial, and robotics automation provides an efficient and safer alternative to manual labor. However, existing robots have limited capabilities in traversing obstacles and lack a mechanical arm for detecting cables and equipment. This study aims to develop an intelligent robot for cable trench inspection, enhancing obstacle-crossing abilities and incorporating a mechanical arm for inspection tasks.

This study presents an intelligent robot for cable trench inspection, featuring a six-degree-of-freedom mechanical arm mounted on a six-track chassis with four flippers. The robot's climbing and obstacle-crossing stability, as well as the motion range of the mechanical arm, are analyzed. The positioning, navigation and remote monitoring systems are developed. Experiments, including climbing and obstacle-crossing performance tests, along with navigation and positioning system tests, are conducted. Finally, the robot's practicability is verified through field testing.

Equipped with flipper tracks, the cable trench inspection robot can traverse obstacles up to 30 cm high and maintain stable locomotion on 30° slopes. Its navigation system enables autonomous operation, while the mechanical arm performs cable current detection tasks. The remote monitoring system provides comprehensive control of the robot and environmental parameter monitoring in cable trenches.

The front and rear flipper tracks enhance the robot's ability to traverse obstacles in cable trenches. The mechanical arm addresses cable current and equipment contact detection issues. The navigation and remote monitoring systems improve the robot's autonomous operation and environmental monitoring capabilities. Implementing this robot can advance the automation and intelligence of cable trench inspections.

Stainless-clad bimetallic steels (SCBS) are widely investigated in some extremely environmental applications areas, such as polar sailing area and tropical oil and gas platforms areas, because of their excellent anticorrosion performance and relatively lower production costs. However, the properties of SCBS, including the mechanical strength, weldability and the anticorrosion behavior, have a direct relation with the manufacturing process and can affect their practical applications. This paper aims to review the application and the properties requirements of SCBS in marine environments to promote the application of this new material in more fields.

In this paper, the manufacturing process, welding and corrosion-resistant properties of SCBS were introduced systematically by reviewing the related literatures, and some results of the authors’ research group were also introduced briefly.

Different preparation methods, such as rolling composite, casting rolling composite, explosive composite, laser cladding and plasma arc cladding, as well as the process parameters, including the vacuum degree, rolling temperature, rolling reduction ratio, volume ratios of liquid to solid, explosive ratio and the heat treatment, influenced a lot on the properties of the SCBS through changing the interface microstructures. Otherwise, the variations in rolling temperature, pass, reduction and the grain size of clad steel also brought the dissimilarities of the mechanical properties, microhardness, bonding strength and toughness. Another two new processes, clad teeming method and interlayer explosive welding, deserve more attention because of their excellent microstructure control ability. The superior corrosion resistance of SCBS can alleviate the corrosion problem in the marine environment and prolong the service life of the equipment, but the phenomenon of galvanic corrosion should be noted as much as possible. The high dilution rate, welding process specifications and heat treatment can weaken the intergranular corrosion resistance in the weld area.

This paper summarizes the application of SCBS in marine environments and provides an overview and reference for the research of stainless-clad bimetallic steel.

In general, the existing compressor design methods require abundant knowledge and inspiration. The purpose of this study is to identify an intellectual design optimization method that enables a machine to learn how to design it.

The airfoil design process was solved using the reinforcement learning (RL) method. An intellectual method based on a modified deep deterministic policy gradient (DDPG) algorithm was implemented. The new method was applied to agents to learn the design policy under dynamic constraints. The agents explored the design space with the help of a surrogate model and airfoil parameterization.

The agents successfully learned to design the airfoils. The loss coefficients of a controlled diffusion airfoil improved by 1.25% and 3.23% in the two- and four-dimensional design spaces, respectively. The agents successfully learned to design under various constraints. Additionally, the modified DDPG method was compared with a genetic algorithm optimizer, verifying that the former was one to two orders of magnitude faster in policy searching. The NACA65 airfoil was redesigned to verify the generalization.

It is feasible to consider the compressor design as an RL problem. Trained agents can determine and record the design policy and adapt it to different initiations and dynamic constraints. More intelligence is demonstrated than when traditional optimization methods are used. This methodology represents a new, small step toward the intelligent design of compressors.

Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.

In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario.

It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.

In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

Drawing upon the theory of information culture from information science and prior research on Chinese information practices. The purpose of this paper is to posit that instead of over-relying on Hofstede’s national cultural values, the IS field should develop theories on national information culture to bridge the theoretical gaps found in explaining the differences in individuals’ IT perceptions and behaviors across cultures.

To achieve the objectives of this study, two quantitative studies were conducted. The first study is exploratory in nature and focuses on the development and calibration of an instrument to measure the Chinese information culture (CIC). The second study takes a confirmatory approach to investigate the impact of the CIC on IT perceptions of manager.

The study develops and validates an instrument for measuring the CIC that comprises information source and format preference, information ownership and analysis style, and centralized decision-making and information control. The CIC is found to influence IT perceptions of managers including perceived performance improvement resulted from IT use, attitude toward using work technologies and attitude on the value of IT in general.

This study is one of the first studies to theorize national information culture to understand IT perceptions at the individual level. The development of the CIC measure provides the much needed methodological support for contextualizing cross-cultural research.

Energy groups are cargo owners with large amounts of energy sources (such as coal) to transport. To achieve a satisfactory tradeoff between the reliability requirements of the sea transportation process and the need to control the investment cost, they usually set up a self-owned fleet supplemented by a chartered fleet. This paper aims to investigate the best fleet structure and to evaluate the investment scheme under volatile circumstances in the shipping market.

The authors construct a mathematical model to determine the ratio of the self-owned fleet to the total fleet to minimize fleet operating costs. The volatility of both freight rates and oil prices is taken into consideration. The CPLEX solver is used to empirically analyze real data from an energy group in China, and the ship investment plan is evaluated considering the technical and economic feasibility.

If the ratio of the self-owned fleet to the total fleet is increased to the optimal of 90.40%, the total operating cost is reduced by 33.98%. Thus, the energy group should increase its capacity with a Panamax vessel of approximately 82,000 DWT. Purchasing a 5-year-old secondhand ship and building a new ship both have good investment return indicators.

For cargo owners engaging in transporting bulk cargo domestically in China, the suggested fleet ratio can provide a reference with a universal application scale, given the boundary economic conditions (including the volatility of freight rates and oil prices in the shipping market) in the paper.

The purpose of this paper is to examine the influences of positive-framed and negative-framed green advertising on pro-environmental WTP. This study also explores the impacts of regulatory focus, environmental concern and pleasant level on green advertising effectiveness.

Data are collected from a within-participant between-group online experiment in China. The generalized estimating equation (GEE) is employed to investigate the impact of green advertising on WTP. Grouped regression and mediation analyses are conducted to explore the influences of regulatory focus, environmental concern and pleasure on advertising efficacy.

The experimental outcomes indicate that green advertising significantly increases participants’ pro-environmental WTP, and negative-framed advertising is more effective than its positive-framed counterpart. Prevention focus heightens receptivity to green advertising, and the relation of environmental concern to advertising effectiveness is inverted U-shaped. Pleasure mediates the effect of green advertising on the WTP, and this mediating role is influenced by emotional intensity when advertising is negatively framed.

Evidence suggests that green advertising may propel pro-environmental WTP by raising environmental awareness, but such a relationship remains severely understudied. As such, this study pioneers in exploring the impact of different-framed green advertising on pro-environmental WTP, extending the concept of green advertising to environmental management. By considering the influences of regulatory focus, environmental concern and pleasure, this study raises practical implications for designing green advertisements, such as increasing the usage of visual elements.

The playable ad is a new type of digital advertising that combines interactivity with gamification. Guided by psychological reactance theory, this study aims to explore the psychological processes and effects of playable ads on consumers’ perceived control and product attitudes.

This paper conducted two experiments to examine the relationship between playable ads, perceived control and product attitude. This paper also applied psychological reactance theory and investigated whether perceived control triggered by the interactive features of playable ads influenced psychological reactance toward them.

Findings from two experiments show that playable ads, compared to video ads, increased consumers’ perceived control, which, in turn, led to more positive attitudes toward the advertised products (Studies 1 and 2). This study also supports psychological reactance theory by revealing that increased perceived control diminished perceived freedom threat and subsequently alleviated consumers’ psychological reactance toward advertising messages (Study 2).

This study sheds light on the effectiveness of a new type of ad-game integration – playable ads. Different from prior research in gamification of advertising, this paper examined the effectiveness of playable ads in an information processing context in which the ads were not the primary task to focus on. This study also extends psychological reactance theory in the context of interactive marketing by exploring the effect of perceived control afforded by digital message features in mitigating reactance.