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In order to help companies better grasp the perceptual needs of consumers for patterns, so as to carry out more accurate product pattern development and recommendation, this research develops a product pattern design system based on computer-aided design.

First, use the Kansei engineering theory and method to obtain the user's perceptual image, and deconstruct and encode the pattern based on the morphological analysis method, then through the BP neural network to construct the mapping relationship between the user's perceptual image and the pattern design elements, and finally calculate and find the corresponding design code combination according to the design goal to guide the pattern design.

Taking costume paper-cut patterns as an example, the feasibility of this system is verified, the design system can well reflect the user's perceptual image in the pattern design and improve the efficiency of pattern customization service.

Compared with the traditional method that relies on the designer's personal experience to propose a design plan, this research provides scientific and intelligent design methods for product pattern design.

The quality of GeOx–Ge interface and the equivalent oxide thickness (EOT) are the main issues in fabricating high-k/Ge gate stack due to the low-k of GeOx interfacial layer (IL). Therefore, a precise study of the formation of GeOx IL and its contribution to EOT is of utmost importance. In this study, the GeOx ILs were formed through post-oxidation annealing of sputtered Al₂O₃ on the Ge substrate. The purpose of this paper is to report on growth kinetics and composition of IL between Al₂O₃ and Ge for HCl- and HF-last Ge surface.

After wet chemical cleaning with HCl or HF, Al₂O₃ was grown onto the Ge surface by RF sputtering. Thickness and composition of IL formed after post-anneal deposition at 400°C in dry oxygen ambience were evaluated as a function of deposition time by FESEM and characterized by X-ray photoelectron spectroscopy, respectively.

It was observed that the composition and thickness of IL were dependent on the starting surface and an aluminum germinate-like composition was formed during RF sputtering for both HF- and HCl-last starting surface.

The novelty of this work is to investigate the starting surface of Ge to IL growth between Al₂O₃/Ge that will lead to the improvement in Ge metal insulator field effect transistors (MISFETs) application.

Shaded‐pole type induction motors are simple structure, strongly‐built and low cost since they have no condensers. Therefore they are used generally in cooling fans in the devices around computers etc., even though they are of low efficiency due to the power loss in the shading coils.

Current flexible printed circuit (FPC) assembly relies heavily on manual labor, limiting capacity and increasing costs. Small FPC size makes automation challenging as terminals can be visually occluded. The purpose of this study is to use 3D tactile sensing to mimic human manual mating skills for enabling sensing offset between FPC terminals (FPC-t) and FPC mating slots (FPC-s) under visual occlusion.

The proposed model has three stages: spatial encoding, offset estimation and action strategy. The spatial encoder maps sparse 3D tactile data into a compact 1D feature capturing valid spatial assembly information to enable temporal processing. To compensate for low sensor resolution, consecutive spatial features are input to a multistage temporal convolutional network which estimates alignment offsets. The robot then performs alignment or mating actions based on the estimated offsets.

Experiments are conducted on a Redmi Note 4 smartphone assembly platform. Compared to other models, the proposed approach achieves superior offset estimation. Within limited trials, it successfully assembles FPCs under visual occlusion using three-axis tactile sensing.

A spatial encoder is designed to encode three-axis tactile data into feature maps, overcoming multistage temporal convolution network’s (MS-TCN) inability to directly process such input. Modifying the output to estimate assembly offsets with related motion semantics overcame MS-TCN’s segmentation points output, unable to meet assembly monitoring needs. Training and testing the improved MS-TCN on an FPC data set demonstrated accurate monitoring of the full process. An assembly platform verified performance on automated FPC assembly.

The purpose of this paper is to estimate the contact-consistent object poses during contact-rich manipulation tasks based only on visual sensors.

The method follows a four-step procedure. Initially, the raw object poses are retrieved using the available object pose estimation method and filtered using Kalman filter with nominal model; second, a group of particles are randomly generated for each pose and evaluated the corresponding object contact state using the contact simulation software. A probability guided particle averaging method is proposed to balance the accuracy and safety issues; third, the independently estimated contact states are fused in a hidden Markov model to remove the abnormal contact state observations; finally, the object poses are refined by averaging the contact state consistent particles.

The experiments are performed to evaluate the effectiveness of the proposed methods. The results show that the method can achieve smooth and accurate pose estimation results and the estimated contact states are consistent with ground truth.

This paper proposes a method to obtain contact-consistent poses and contact states of objects using only visual sensors. The method tries to recover the true contact state from inaccurate visual information by fusing contact simulations results and contact consistency assumptions. The method can be used to extract pose and contact information from object manipulation tasks by just observing the demonstration, which can provide a new way for the robot to learn complex manipulation tasks.

As complex analysis of contact models is required in the traditional assembly strategy, it is still a challenge for a robot to complete the multiple peg-in-hole assembly tasks autonomously. This paper aims to enable the robot to complete the assembly tasks autonomously and more efficiently, with the strategies learned by reinforcement learning (RL), a learning-accelerated deep deterministic policy gradient (LADDPG) algorithm is proposed.

The multiple peg-in-hole assembly strategy is designed in two modules: an advanced planning module and a bottom control module. The advanced module is completed by the LADDPG agent, which is used to derive advanced commands based on geometric and environmental constraints, that is, the desired contact force. The bottom-level control module will drive the robot to complete the compliant assembly task through the adaptive impedance algorithm according to the command set issued by the advanced module. In addition, a set of safety assurance mechanisms is developed to safely train a collaborative robot to complete autonomous learning.

The method can complete the assembly tasks well through RL, and it can realize satisfactory compliance of the robot to the environment. Compared with the original DDPG algorithm, the average values of the instantaneous maximum contact force and contact torque during the assembly process are reduced by approximately 38% and 74%, respectively.

The entire algorithm can also be applied to other robots and the assembly strategy can be applied in the field of the automatic assembly.

A compliant assembly strategy based on the LADDPG algorithm is proposed to complete the automated multiple peg-in-hole assembly tasks.

This study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator.

A closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection.

Physical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models.

Constraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities.

By eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems.

This research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

Nowadays, there is a high demand for online services and applications. However, there is a challenge to keep these applications secured by applying different methods rather than using the traditional approaches such as passwords and usernames. Keystroke dynamics is one of the alternative authentication methods that provide high level of security in which the used keyboard plays an important role in the recognition accuracy. To guarantee the robustness of a system in different practical situations, there is a need to examine how much the performance of the system is affected by changing the keyboard layout. This paper aims to investigate the impact of using different keyboards on the recognition accuracy for Arabic free-text typing.

To evaluate how much the performance of the system is affected by changing the keyboard layout, an experimental study is conducted by using two different keyboards which are a Mac’s keyboard and an HP’s keyboard.

By using the Mac’s keyboard, the results showed that the false rejection rate (FRR) was 0.20, whilst the false acceptance rate (FAR) was 0.44. However, these values have changed when using the HP’s keyboard where the FRR was equal to 0.08 and the FAR was equal to 0.60.

The number of participants in the experiment, as the authors were targeting much more participants.

These results showed for the first time the impact of the keyboards on the system’s performance regarding the recognition accuracy when using Arabic free-text.

The purpose of this study is to report and contrast manufacturer–supplier relationships, supplier selection and procurement performance of two manufacturing sectors in Kuwait. The effect of supplier relationship and selection on the performance of the procurement function was also investigated.

Surveys of supplier selection, supplier relationship and procurement performance are taken from 62 plants operating in 2 competitive manufacturing sectors in Kuwait (foods industry and refractors industry). The study utilizes multivariate and multi-regression analyses in understanding the effect of supplier relationship and selection on the performance of the procurement function.

Findings indicate a significant effect of supplier relationship and supplier selection on a plant’s procurement performance. However, variance in plant size and/or industrial sector was found to not affect this relationship.

Despite the significance of the Gulf states and the growing importance of the manufacturing sector in these countries, relatively little is known about how buyers and suppliers within this sector interact. This study is the first to document supplier relationships and selection processes in this area of the world. The study also provides a reliable and valid scale for measuring the performance of the procurement function in the Kuwaiti manufacturing sector. Furthermore, the postulated impact of supplier–buyer relationships on the performance of the procurement functions was investigated in a newly emerging economy in the Gulf.