Optimization of power generating thermoelectric modules utilizing LNG cold energy

A theoretical investigation to optimize thermoelectric modules, which convert LNG cold energy into electrical power, is performed using a novel one-dimensional analytic model. In the model the optimum thermoelement length and external load resistance, which maximize the energy conversion ratio, are determined by the heat supplied to the cold heat reservoir, the hot and cold side temperatures, the thermal and electrical contact resistances and the properties of thermoelectric materials. The effects of the thermal and electrical contact resistances and the heat supplied to the cold heat reservoir on the maximum energy conversion ratio, the optimum thermoelement length and the optimum external load resistance are shown.     

Association between pain intensity in the neck and components of a workstation: A cross-sectional study on Japanese office workers

This study aimed to investigate the association between inappropriate workstation components and neck pain intensity. This cross-sectional study was conducted on 309 Japanese office workers. Workstation questionnaires were developed based on previous studies and consisted of 11 items that assessed the armrest, monitor screen, desk, and keyboard. For each of the 11 items, we defined whether the item was “inadequate” based on previous studies. Neck pain experienced was measured using a numerical rating scale (NRS) of 0–10, and the NRS scores were categorized into three (0: “no pain,” 1–3: “low pain intensity,” and 4–10: “high pain intensity”). Crude and adjusted ordered logistic regressions were used to determine the association between the inadequate workstation and neck pain intensity. In the first analysis model, each item of the workstation questionnaire was used as an explanatory variable. Then, in the second analysis model, the number of inadequate workstation components was used as the explanatory variable. Ordered logistic regression analyses showed that the distance of the eye to the monitor was significantly associated with neck pain intensity (crude, OR: 2.21; 95% CI: 1.41–3.19; adjusted, OR: 1.79; 95%CI: 1.08–2.50). The number of inadequate workstation components had a significant positive association with neck pain intensity (crude, OR: 1.19, 95%CI: 1.04–1.34; adjusted, OR: 1.15, 95% CI: 1.00–1.32). The results of this study suggest that the distance between the eye and the monitor might be the most important workstation factor contributing to neck pain, and workstation components should be assessed collectively.     

Factors influencing the perceived usability of wearable chair exoskeleton with market segmentation: A structural equation modeling and K-Means Clustering approach

The sudden implementation of work-from-home setup has caused the rise of sedentary position among employees which led to an increase in development or worsening of musculoskeletal disorders. To help mitigate this problem, wearable assistive devices such as wearable chairs have been argued to be beneficial. Several studies have expounded on the benefit of using this, however little to no available device is yet present in the Philippines. To which, businesses and developers could capitalize on this aspect. This study examined the perceived usability of wearable chair exoskeleton in the Philippines by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT2) and System Usability Scale (SUS). A questionnaire was developed and distributed to 365 residents in the Philippines to measure the factors and their relationship to perceived usability accurately. Indicators and measures used in the questionnaire were derived from existing literature on technology acceptance and usability evaluation. Partial least squares structural equation modeling (PLS-SEM) was used for this study. Results revealed that perceived usability was significantly influenced by usage behavior. At the same time, habit, hedonic motivation, and performance expectancy significantly influenced the behavioral intention of the wearable chair exoskeleton. K-Means clustering was also utilized to identify clusters of target users of the wearable chair in the country, such as high-value customers, core customers, and low-value customers. Results indicated that demographic factors such as females, 30 years old and below, earning Php45,000 and above monthly, and living in a city are highly likely to utilize the product. The findings of this study can be applied and extended as a theoretical framework for future researchers of consumer behavior and exoskeleton developers for enhancing the innovation and usability of this new technology. This study may also be used and capitalized by investors to strategize the development and marketing of the exoskeletons among industrial and teleworkers worldwide.     

Influence mechanism of icon semantics on visual search performance: Evidence from an eye-tracking study

Explicit icon semantics can reduce the difficulty of understanding complex visual information. Optimizing the icon semantics and text semantics of icons can effectively improve the cognitive performance of digital interfaces. This paper adopts visual search tasks to study the effects of different combinations of icon semantic familiarity and the presence or absence of text on icon search performance under horizontal and vertical layouts. The behavioral experiment results show that under two layouts: 1. The main effect of icon semantics is significant, and the search performance increases with the increase of semantic familiarity. 2. The main effect of text is significant, and the search performance is negatively correlated with the addition of text. The eye movement experiment found that the semantic familiarity of icons had a significant impact on average fixation time. Furthermore, the number of fixation points changed significantly after the text variable was added. Therefore, there was no significant difference in the number of fixation points in the horizontal layout, and icon semantics was the main influencing factor in visual search. In the vertical layout, there was no significant difference in average fixation time, and text was the main influencing factor of visual search. The results show that the semantic familiarity of icons and different combinations with or without text significantly affect visual search performance in horizontal and vertical layouts. This paper provides a theoretical reference for the combination of icons and text in interface design.     

强化学习与自适应动态规划:从基础理论到多智能体系统中的应用进展综述

近年来,强化学习与自适应动态规划算法的迅猛发展及其在一系列挑战性问题(如大规模多智能体系统优化决策和最优协调控制问题)中的成功应用,使其逐渐成为人工智能、系统与控制和应用数学等领域的研究热点.鉴于此,首先简要介绍强化学习和自适应动态规划算法的基础知识和核心思想,在此基础上综述两类密切相关的算法在不同研究领域的发展历程,着重介绍其从应用于单个智能体(控制对象)序贯决策(最优控制)问题到多智能体系统序贯决策(最优协调控制)问题的发展脉络和研究进展.进一步,在简要介绍自适应动态规划算法的结构变化历程和由基于模型的离线规划到无模型的在线学习发展演进的基础上,综述自适应动态规划算法在多智能体系统最优协调控制问题中的研究进展.最后,给出多智能体强化学习算法和利用自适应动态规划求解多智能体系统最优协调控制问题研究中值得关注的一些挑战性课题.     

Development of new shoe sizing system for women based on regression analysis of foot shapes

This study aims to demonstrate a new method of developing a shoe sizing system with a standard fitting for each size for Bangladeshi women based on foot measurements. In this study, bivariate correlation analysis was carried out to determine key foot dimensions of 976 women aged 20 to 60. Simple linear regression analyses of key parameters against foot length (FL) were conducted, and the regression equations assisted in determining grading value and size-fit combinations. Nine sizes with three fittings (narrow, standard, and wide) each were generated where the grading values were 6 mm, 5 mm, 2 mm, and 4 mm for FL, joint girth (JG), joint width (JW), and arch length (AL), respectively. Cross-tabulation analysis verified the sizing system with a coverage rate 94.98% of JG, 88.02% of JW, and 98.77% of AL, where standard fittings covered the maximum number of participants. This study could benefit women in choosing accurate shoe sizes for their feet to ensure proper shoe fitting.Relevance to industryThe proposed new shoe sizing system could assist the footwear industries in manufacturing women's shoes in different sizes with appropriate sizing and grading values, which will provide better fitting than existing systems. In addition, industries could produce shoes with a smaller number of size-fit combinations to accommodate most women's feet.     

DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism

Objective.Computer vision-based up-to-date accurate damage classification and localization are of decisive importance for infrastructure monitoring, safety, and the serviceability of civil infrastructure. Current state-of-the-art deep learning (DL)-based damage detection models, however, often lack superior feature extraction capability in complex and noisy environments, limiting the development of accurate and reliable object distinction.Method.To this end, we present DenseSPH-YOLOv5, a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone to improve in preserving and reusing critical feature information. Additionally, convolutional block attention modules (CBAM) have been implemented to improve attention performance mechanisms for strong and discriminating deep spatial feature extraction that results in superior detection under various challenging environments. Moreover, an additional feature fusion layers and a Swin-Transformer Prediction Head (SPH) have been added leveraging advanced self-attention mechanism for more efficient detection of multiscale object sizes and simultaneously reducing the computational complexity.Results.Evaluating the model performance in large-scale Road Damage Dataset (RDD-2018), at a detection rate of 62.4 FPS, DenseSPH-YOLOv5 obtains a mean average precision (mAP) value of 85.25%, F1-score of 81.18%, and precision (P) value of 89.51% outperforming current state-of-the-art models.Significance.The present research provides an effective and efficient damage localization model addressing the shortcoming of existing DL-based damage detection models by providing highly accurate localized bounding box prediction. Current work constitutes a step towards an accurate and robust automated damage detection system in real-time in-field applications.     

基于深度强化学习的风电柔直并网系统次同步振荡抑制方法

风电柔性直流换流站与风机交互导致的次同步振荡严重威胁电网安全稳定运行。深度强化学习算法可以有效地应对风电柔直并网系统多变的运行工况。提出了一种基于深度强化学习的振荡抑制方法。首先,基于柔直送端变流器的数学模型和机理,设计了系统的环境状态合集、可行动作集及奖励函数。为了应对所设计的环境状态合集中的电流变量及电压变量均为连续量带来的维数灾问题,继而采用深度确定性策略梯度算法进行可行动作集的动作决策探索。最后通过仿真系统在多变运行工况下对该方法的有效性和鲁棒性进行验证,仿真结果表明所提方法能够充分地适应海上风电柔直并网系统的多风速运行条件,并能在短时间内有效抑制振荡。     

基于3D打印技术的梯形凹槽双脊喇叭天线

该文提出了一种带有梯形槽的双脊喇叭天线,天线在口径面和外侧壁引入梯形槽结构,抑制了喇叭外壁上的电流,从而达到降低喇叭天线旁瓣的目的。天线采用3D打印技术,有效地解决了传统方式加工难及一致性差的问题。仿真和实测表明,该天线设计可有效提高天线增益,在工作频带 5~10 GHz内天线增益大于 10．32 dBi,回波损耗小于-10dB;在9．6 GHz时峰值增益可达14．61 dBi。将天线喇叭壁设计成空心结构,天线质量减小了74%。测试结果与仿真结果基本一致。     

A hypernetwork-based context-aware approach for design lesson-learned knowledge proactive feedback in design for manufacturing

Design lesson-learned knowledge (DLK) clearly describes various design quality problems exposed in past manufacturing stages, solutions and preventive measures. If knowledge management system could proactively feed DLK back into design process, many previous quality problems would be avoided, thus helping designers better implement design for manufacturing (DFM) in a smarter manner. However, since design quality problems are not pointed out in design process, problem-relevant information extracted from design contexts might be inaccurate. In this situation, traditional context-aware approach is prone to acquire design quality problems that designers do not need. Facing these challenges, a hypernetwork-based context-aware DLK proactive feedback approach is proposed to construct hypernetwork-based DLK representation model and predict possible design quality problems in the design process, thus providing corresponding DLK and helping designers reduce the reoccurrence of previous quality problems in DFM. Specifically, hypernetwork-based DLK representation model is first constructed, which consists of designer context network, task context network and DLK network. Based on this model, a context-aware collaborative reason strategy is constructed to predict possible design quality problems according to complex design contexts. To validate the proposed approach, a practical product development case on the shipbuilding design is implemented, and some comparative experiments are conducted. Experiment results show the proposed approach is effective and has a positive performance in DFM. It is anticipated this work opens up a promising way to help designers reuse DLK for reducing the reoccurrence of previous design quality problems in a smarter manner, thus better implementation of DFM.