COURSE-KEEPING CONTROL FOR DIRECTIONALLY UNSTABLE LARGE TANKERS USING THE MIRROR-MAPPING TECHNIQUE

This study examines the course-keeping control of directionally unstable large oil tankers involving a pole in the right half plane. Treated as an unstable plant in control engineering, tankers are theoretically and experimentally investigated during the controller design process. First, the unstable plant is mirror-mapped to its corresponding stable minimum phase plant using the mirror-mapping technique, which enables an easy controller design. Then, a linear proportional-differential and a first-order filter controller is designed based on the closed-loop gain shaping algorithm, which requires only one controller parameter to be properly selected based on the system’s characteristics. Numerical simulation results confirmed that the designed controller can successfully stabilise an unstable plant subjected to external wind and wave disturbances. The controller designed with the proposed method is suitable for course-keeping control of directionally unstable large tankers. The controller design method is simple with an uncomplicated structure that can easily be implemented in engineering endeavours. Moreover, the rudder motion is small and soft

Microstructure and Wear Resistance of Laser Cladding of Fe-Based Alloy Coatings in Different Areas of Cladding Layer

In this study, laser cladding technology was used to prepare Fe-based alloy coating on a 27SiMn hydraulic support, and a turning treatment was used to obtain samples of the upper and middle regions of the cladding layer. The influence of microstructure, phase composition, hardness, and wear resistance in different areas of the cladding layer was studied through scanning electron microscopy (SEM), X-ray diffractometry (XRD), friction and wear tests, and microhardness. The results show that the bcc phase content in the upper region of the cladding layer is less than that in the middle region of the cladding layer, and the upper region of the cladding layer contains more metal compounds. The hardness of the middle region of the cladding layer is higher than that of the upper region of the cladding layer. At the same time, the main wear mechanism of the upper region of the cladding layer is adhesive wear and abrasive wear. The wear mechanism of the middle region of the cladding layer is mainly abrasive wear, with better wear resistance than the upper region of the cladding layer

Gaseous Emissions from a Seagoing Ship under Different Operating Conditions in the Coastal Region of China

Pollution caused by ship emissions has drawn attention from various countries. Because of the high density of ships in ports, channels, and anchorages and their proximity to the densely populated areas, ship emissions will considerably impact these areas. Herein, a Chinese seagoing ship is selected and a platform is established for monitoring the ship emissions to obtain detailed characteristics of the ship’s nearshore emissions. The ship navigation and pollution emission data are obtained under six complete operating conditions, i.e., berthing, manoeuvring in port, acceleration in a channel, cruising, deceleration before anchoring, and anchoring. This study analyzes the concentrations of the main emission gases (O2, NOX, SO2, CO2, and CO) and the average emission factors (EFs) of the pollution gases (NOX, SO2, CO2, and CO) based on the engine power under different operating conditions. Results show that the change in O2 concentration reflects the load associated with the main engine of the ship. The NOX, SO2, and CO2 emission concentrations are the highest during cruising, whereas the peak CO emission concentration is observed during anchoring. The average EFs of NOX and SO2 based on the power of the main engine are the highest during cruising, and those of CO2 and CO are the highest after anchoring. The ship EFs are different during acceleration and deceleration. By comparing the EFs along the coast of China and the global EFs commonly used to perform the emission inventory calculations in China, the NOX EFs under different operating conditions is observed to be generally lower than the global EFs under the corresponding operating conditions. Furthermore, the SO2 EF is considerably affected by the sulfur content in the fuel oil and the operating conditions of the ship. The average CO2 EFs are higher than the global EFs commonly used during cruising, and the CO EFs are higher than the global EFs under all the conditions. Our results help to supplement the EFs for this type of ship under different operating conditions, resolve the lack of emission data under anchoring conditions, and provide data support to conduct nearshore environmental monitoring and assessment

Study of the Microstructure and Corrosion Properties of a Ni-Based Alloy Coating Deposited onto the Surface of Ductile Cast Iron Using High-Speed Laser Cladding

To improve the surface corrosion resistance of ductile iron, Ni-based alloy coatings were prepared using a high-speed laser cladding technology with different levels of laser power. The microstructure, phases, and corrosion properties of the coatings were investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and an electrochemical workstation. Variations in laser power did not change the main phases of the coatings, which were composed of γ-Ni, Ni3B, Ni2Si, and Cr23C6. With an increase in power, the degree of segregation in the coating decreased, sufficient melting between elements was achieved, and the chemical composition became more uniform. Enhancement of the laser power resulted in more energy being injected into the cladding, which allowed adequate growth of tissue, and dendrites continued to grow in size as the power increased. The self-corrosion potentials of the coatings at laser power levels of 1.6, 2.0, and 2.4 kW were −625.7, −526.5, and −335.7 mV, respectively. The corrosion potential of the 2.4 kW coating was the highest, and the corroded surface of the cladding layer included mainly sizeable continuous structures with a light degree of corrosion and the highest corrosion resistance

X3DFast model for classifying dairy cow behaviors based on a two-pathway architecture

Abstract Behavior is one of the important factors reflecting the health status of dairy cows, and when dairy cows encounter health problems, they exhibit different behavioral characteristics. Therefore, identifying dairy cow behavior not only helps in assessing their physiological health and disease treatment but also improves cow welfare, which is very important for the development of animal husbandry. The method of relying on human eyes to observe the behavior of dairy cows has problems such as high labor costs, high labor intensity, and high fatigue rates. Therefore, it is necessary to explore more effective technical means to identify cow behaviors more quickly and accurately and improve the intelligence level of dairy cow farming. Automatic recognition of dairy cow behavior has become a key technology for diagnosing dairy cow diseases, improving farm economic benefits and reducing animal elimination rates. Recently, deep learning for automated dairy cow behavior identification has become a research focus. However, in complex farming environments, dairy cow behaviors are characterized by multiscale features due to large scenes and long data collection distances. Traditional behavior recognition models cannot accurately recognize similar behavior features of dairy cows, such as those with similar visual characteristics, i.e., standing and walking. The behavior recognition method based on 3D convolution solves the problem of small visual feature differences in behavior recognition. However, due to the large number of model parameters, long inference time, and simple data background, it cannot meet the demand for real-time recognition of dairy cow behaviors in complex breeding environments. To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. We recorded videos of dairy cow behaviors at a dairy farm containing over one hundred cows using surveillance cameras. A robust model was built using a complex background dataset. We proposed a two-pathway X3DFast model based on spatiotemporal behavior features. The X3D and fast pathways were laterally connected to integrate spatial and temporal features. The X3D pathway extracted spatial features. The fast pathway with R(2 + 1)D convolution decomposed spatiotemporal features and transferred effective spatial features to the X3D pathway. An action model further enhanced X3D spatial modeling. Experiments showed that X3DFast achieved 98.49% top-1 accuracy, outperforming similar methods in identifying the four behaviors. The method we proposed can effectively identify similar dairy cow behaviors while improving inference speed, providing technical support for subsequent dairy cow behavior recognition and daily behavior statistics

3D Mosaic Carbon Nanofiber Sensors for Room‐Temperature Detection of Methane and Other Dissolved Gases in Transformer Oil

Abstract Hydrocarbons and carbon oxides are typical dissolved gases in transformer oil that reflect the latent pitfalls, on‐line monitoring of their concentrations can effectively evaluate the operating status of the power transformer. However, these low‐concentration targets (especially for CH4) show high chemical inertness at room temperature, challenging the sensitive performance of current commonly used chemiresistive gas sensors. Herein, a strategy by combining traditional inorganic semiconductors to carbon nanofiber (CNF) via electrospinning–annealing route is described. Three optimized 3D mosaic films, CNFs scaffold incorporated with WO3, SnO2 and MoS2 nanoparticles, are obtained. Due to the large specific surface area of the 3D network, and the synergic and heterojunction effects between nanoparticles and CNFs, all three sensors exhibit high response to CH4 at room temperature, and also record distinguishable signals toward H2, C2H4, CO and CO2, revealing the three sensors are cross‐sensitive to the five analytes. Accordingly, preliminary discrimination of five dissolved gases is realized by principle component analysis. This study provides an effective and extendable solution of preparing room‐temperature chemiresistive sensors for the detection of CH4 and other gases, and offers a strategy for the construction of sensor array to achieve a high discrimination capability

Prediction of a Stable Organic Metal-Free Porous Material as a Catalyst for Water-Splitting

It is of practical significance to find organic metal-free catalyst materials. We propose a new graphene-like carbon nitride structure, which was able to meet these requirements well. Its primitive cell consists of eight carbon atoms and six nitrogen atoms. Hence, we called this structure g–C8N6. The stability of the structure was verified by phonon spectroscopy and molecular dynamics simulations. Then its electronic structure was calculated, and its band edge position was compared with the redox potential of water. We analyzed its optical properties and electron–hole recombination rate. After the above analysis, it is predicted that it is a suitable photocatalyst material. To improve its photocatalytic performance, two methods were proposed: applied tensile force and multilayer stacking. Our research is instructive for the photocatalytic application of this kind of materials