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| 1 | 几何增强的装配工艺本体建模显示文摘包含产品几何与非几何信息的产品装配要求是装配工艺设计的主要依据,对装配工艺设计决策结果具有决定性的影响。在装配工艺模型中描述产品的几何相关信息能够更好地表达如装配顺序,装配路径等内容,同时可以更好地辅助装配工艺决策,提高决策的自动化水平。提出一种几何增强的装配工艺本体模型,利用语义本体技术,建立产品几何信息在装配工艺中的描述方法,数据结构与关系描述。并以该模型为基础,建立基于几何增强装配工艺本体的装配工艺决策框架。该框架分为两部分:一是采用几何增强的装配工艺本体模型描述产品几何信息和装配工艺信息的数据结构和相互关系;二是为了让几何信息与装配工艺信息之间存在的丰富的隐含关系显性化,对语义本体推理机制进行增强,提出推理单元的概念,使决策框架具有从产品几何信息中推理装配工艺相关信息的能力,有效地支持装配工艺决策。以针对某传动器产品装配顺序决策的过程,验证所提出的几何增强装配工艺本体模型与框架的有效性。 | 乔立红 朱怡心 ANWER Nabil | 2015 | 机械工程学报2015，51，22： | 6 |
| 2 | 基于本体的装配过程几何仿真框架显示文摘为了辅助装配工艺设计和验证,提出一种装配过程几何快速仿真本体框架,以本体模型驱动代替传统数据模型驱动,实现装配过程几何仿真自动建模。通过建立具有一般性的装配过程几何仿真本体模型,解决传统数据模型难以表达装配过程几何仿真中概念的复杂关联和知识表达的问题。在装配过程几何仿真本体模型的基础上,进一步研究本体知识决策的机制,实现仿真建模过程中的自动决策,有效地减少仿真建模的手工工作量。以飞机某翼盒组件的装配过程几何仿真为例,对装配仿真过程中典型的装配顺序决策问题进行决策,验证了所提框架的有效性。 | 朱怡心 乔立红 Nabil Anwer | 2013 | 计算机集成制造系统2013，19，5： | 2 |
| 3 | Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People显示文摘Text-To-Speech(TTS)is a speech processing tool that is highly helpful for visually-challenged people.The TTS tool is applied to transform the texts into human-like sounds.However,it is highly challenging to accomplish the TTS out-comes for the non-diacritized text of the Arabic language since it has multiple unique features and rules.Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language.But,such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them from the context itself.In this background,the current research article introduces an Optimal Deep Learning-driven Arab Text-to-Speech Synthesizer(ODLD-ATSS)model to help the visually-challenged people in the Kingdom of Saudi Arabia.The prime aim of the presented ODLD-ATSS model is to convert the text into speech signals for visually-challenged people.To attain this,the presented ODLD-ATSS model initially designs a Gated Recurrent Unit(GRU)-based prediction model for diacritic and gemination signs.Besides,the Buckwalter code is utilized to capture,store and display the Arabic texts.To improve the TSS performance of the GRU method,the Aquila Optimization Algorithm(AOA)is used,which shows the novelty of the work.To illustrate the enhanced performance of the proposed ODLD-ATSS model,further experi-mental analyses were conducted.The proposed model achieved a maximum accu-racy of 96.35%,and the experimental outcomes infer the improved performance of the proposed ODLD-ATSS model over other DL-based TSS models. | Mrim M.Alnfiai Nabil Almalki Fahd N.Al-Wesabi Mesfer Alduhayyem Anwer Mustafa Hilal Manar Ahmed Hamza | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 4 | Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons显示文摘Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches. | Nabil Almalki Mrim M.Alnfiai Fahd N.Al-Wesabi Mesfer Alduhayyem Anwer Mustafa Hilal Manar Ahmed Hamza | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 5 | IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People显示文摘Visual impairment is one of the major problems among people of all age groups across the globe.Visually Impaired Persons(VIPs)require help from others to carry out their day-to-day tasks.Since they experience several problems in their daily lives,technical intervention can help them resolve the challenges.In this background,an automatic object detection tool is the need of the hour to empower VIPs with safe navigation.The recent advances in the Internet of Things(IoT)and Deep Learning(DL)techniques make it possible.The current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNetbased object detection(TSOLWR-ODVIP)model to help VIPs.The primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to them.For data acquisition,IoT devices are used in this study.Then,the Lightweight RetinaNet(LWR)model is applied to detect objects accurately.Next,the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR model.Finally,the Long Short-Term Memory(LSTM)model is exploited for classifying objects.The performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects,and the results were examined under distinct aspects.The comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects,enhancing the quality of life of VIPs. | Mesfer Alduhayyem Mrim M.Alnfiai Nabil Almalki Fahd N.Al-Wesabi Anwer Mustafa Hilal Manar Ahmed Hamza | 2023 | Computer Systems Science & Engineering2023，46，7： | 0 |
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