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The present study is concerned with the application of a nonlinear frequency analysis approach to the detection and location of water tree degradation of power cable XLPE insulation without turning off electric power.

The use of power cable system responses to power line carrier signals are proposed to conduct the required signal analysis for damage location purpose. This technique is based on the fact that the water tree degradation in power cables can make the system behave nonlinearly. Consequently, the location of water tree degradation can be determined by detecting the position of nonlinear components in power cable systems.

A novel method has been proposed for locating water tree degradation in power cable systems; numerical simulation studies have demonstrated the effectiveness of the new technique.

The proposed technique has the potential to be applied in practice to more effectively resolve the power cable damage location problem.

The Bernoulli gripper fixedly installed on the manipulator is subject to limitations such as a small-working region and poor anti-interference capacity. This paper aims to propose a novel Bernoulli gripper design that involves the connection of a positive stiffness component such as a spring in series, based on the force characteristic curve synthesis method, to optimize the mechanical performance.

The proposed gripper is designed and manufactured. In the suction procedure, the force characteristic curve of the proposed gripper is theoretically and experimentally investigated. In the hovering detection procedure, a dynamic model of the manipulator-gripper-workpiece system is established, and an apparatus is set up to compare the displacements of the workpiece and the manipulator. The proposed gripper is finally applied in the lifting procedure, showing good impact resistance.

The optimization of mechanical performance of the proposed gripper is realized. The proposed gripper has the effect of increasing the stiffness of the negative stiffness part of the force characteristic curve and reducing the stiffness of the positive stiffness part, increasing the working region. The stability and the anti-interference ability of the workpiece under high-frequency vibration are improved. Meanwhile, the impact resistance in the lifting procedure is enhanced, compared with the original one.

This research proposes a novel design for the Bernoulli grippers to optimize the mechanical performance. The proposed gripper has advantages of a larger working region, better anti-interference ability and better impact resistance. These findings serve as important theoretical and experimental references for the design of the Bernoulli gripper.

1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.

A novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.

Sandwich structures with well-designed cellular cores exhibit superior shock resistance compared to monolithic structures of equal mass. This study aims to develop a comprehensive analytical model for predicting the dynamic response of cellular-core sandwich structures subjected to shock loading and investigate their application in protective design.

First, an analytical model of a clamped sandwich beam for over-span shock loading was developed. In this model, the incident shock-wave reflection was considered, the clamped face sheets were simplified using two single-degree-of-freedom (SDOF) systems, the core was idealized using the rigid-perfectly-plastic-locking (RPPL) model in the thickness direction and simplified as an SDOF system in the span direction. The model was then evaluated using existing analytical models before being employed to design the sandwich-beam configurations for two typical engineering applications.

The model effectively predicted the dynamic response of sandwich panels, especially when the shock-loading pulse shape was considered. The optimal compressive cellular-core strength increased with increasing peak pressure and shock-loading impulse. Neglecting the core tensile strength could result in an overestimation of the optimal compressive cellular-core strength.

A new model was proposed and employed to optimally design clamped cellular-core sandwich-beam configurations subjected to shock loading.

Corporate diversification is a strategy that enables corporations to expand their core business into other businesses. In Malaysia, corporate diversification continues to represent a fundamental organizational structure. Some two-thirds of Malaysian firms are diversified. However, when compared to developed countries such as the US and the UK, we find that firms are moving toward non-diversification. The study is based on the population framework consisting of all of the public limited companies (PLCs) listed on the Bursa Malaysia stock exchange from 2007 to 2012. A dynamic panel model system generalized method of moments (GMM) was used to analyze the diversification and firm’s performance theories.

The empirical findings demonstrated that diversification is better than non-diversification firms for the curvilinear relationship between diversification and firm’s performance (ROA and Tobin-Q) when using the entropy index and relatedness is taken into consideration. The research further concluded that related and unrelated diversification also has a positive relationship with performance, but diversification must be the dominant (focused) and cannot be too broad in nature. Diversification that is too broad may cause a positive relationship to turn in to a negative relationship toward performance in both related and unrelated instances of diversification.

In autonomous driving, the inherent sparsity of point clouds often limits the performance of object detection, while existing multimodal architectures struggle to meet the real-time requirements for 3D object detection. Therefore, the main purpose of this paper is to significantly enhance the detection performance of objects, especially the recognition capability for small-sized objects and to address the issue of slow inference speed. This will improve the safety of autonomous driving systems and provide feasibility for devices with limited computing power to achieve autonomous driving.

BRTPillar first adopts an element-based method to fuse image and point cloud features. Secondly, a local-global feature interaction method based on an efficient additive attention mechanism was designed to extract multi-scale contextual information. Finally, an enhanced multi-scale feature fusion method was proposed by introducing adaptive spatial and channel interaction attention mechanisms, thereby improving the learning of fine-grained features.

Extensive experiments were conducted on the KITTI dataset. The results showed that compared with the benchmark model, the accuracy of cars, pedestrians and cyclists on the 3D object box improved by 3.05, 9.01 and 22.65%, respectively; the accuracy in the bird’s-eye view has increased by 2.98, 10.77 and 21.14%, respectively. Meanwhile, the running speed of BRTPillar can reach 40.27 Hz, meeting the real-time detection needs of autonomous driving.

This paper proposes a boosting multimodal real-time 3D object detection method called BRTPillar, which achieves accurate location in many scenarios, especially for complex scenes with many small objects, while also achieving real-time inference speed.

This study develops a multi-period structural model to value bank subordinated debt (subdebt) under different regulatory policies. The model provides a complete framework for analyzing how various factors, such as credit and interest rate risks, bank characteristics, and regulatory policies, affect subdebt prices and yield spreads. It finds that the implementation of Prompt Corrective Action (PCA) will raise subdebt prices and lower subdebt spreads, while capital forbearance will have the opposite effects. Also, subdebt spreads are less sensitive to bank risk when PCA is imposed than when capital forbearance occurs. The results of the paper suggest that enhancing market discipline through giving subdebt investors more rights to force timely reorganization of weak banks will reduce the subdebt spreads required by investors.

Reciprocating compressors offer an efficient method of compressing almost any gas composition in a wide range of pressures and have numerous applications. Condition monitoring of critical rotating machinery is widely accepted by operators of centrifugal compressors. However, condition monitoring of reciprocating machinery has not received the same degree of acceptance. An earlier study (Townsend et al., 2016) was conducted on temperature monitoring. The purpose of this paper is to examine the impact of continuous pressure monitoring on electric-driven compressors.

This research analyzes the impact of continuous pressure monitoring on a fleet of 14 compressors transporting CO₂ for enhanced oil recovery. The reliability and efficiency data on 14 reciprocating compressors over a three-year period were analyzed for failures detectable by the condition monitoring technology. The engineering economic analysis is presented to determine the impact this technology will have on the productivity of the compressors.

The study considers utilizing condition monitoring technology to analyze the pressure of the swept volume of the compressor cylinders. The results of the study indicate that continuous pressure monitoring technology has a strong impact on the productivity of the compressor fleet. The internal rate of return not only exceeds the operators hurdle rate, but the payback period is also dramatic. Pressure monitoring was found to be economically better than temperature monitoring.

The study reveals the economic benefits of implementing condition monitoring in the form of continuous pressure monitoring on reciprocating compressors.