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Now showing 1 - 10 of 52

Virtual Reality Exposure Therapy for Driving Phobia Disorder: System Design and Development
(2020-07-01)
Trappey, Amy
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Trappey, Charles V.
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Chang, Chia-Ming
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Kuo, Routine R. T.
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Lin, Aislyn P. C.
Driving phobia is an anxiety disorder. People are greatly impaired in their daily lives when suffering from driving phobia disorders. The anxieties can be triggered under various conditions, such as driving over bridges, driving at high speeds, or driving in close proximity to large trucks. Traditional cognitive behavioral therapy (CBT) and exposure therapy are the most common approaches used in the treatment of psychological disorders, such as anxiety disorder (AD) and panic disorder (PD). This research focuses on virtual reality (VR)-based exposure therapy, called VRET, and describes the design and development of a system which uses alternating levels of fear-based driving scenarios that can be recorded and automatically adjusted to maximize exposure effectiveness without causing the subjects to panic. The proposed VRET integrates an advanced feedback database module for tracing and analyzing the system, along with the user's bio-data to show the valid data collection of the system and its effectiveness for future use in clinical trials. The research conducts a system's pre-test analysis using 31 subjects to demonstrate the effectiveness of the system. This research demonstrates the systematic development of the VRET for driving phobia disorder by depicting the system framework, key system modules, system integration, bio-database management, and pre-test data analysis to support our next research efforts in hospital-based clinical trials and for additional VRET development applications for clinical psychology.
Detecting Trademark Image Infringement Using Convolutional Neural Networks
(2019-01-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Lin, Sam C-C.
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管理科學系
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Department of Management Science
The rapid development of consumer products with short life spans, along with fast, global e-commerce and e-marketing distribution of products and services requires greater corporate diligence to protect intangible assets such as brands which can easily be coped or placed in grey markets. Trademarks are the government registered legal intellectual property rights (IPRs) used to protect companies' brands and build brand equity. Given the rapid growth in the number of global trademark registrations, the number of trademark infringement cases is also increasing, a great challenge for the original trademark owner to detect the infringement and takes action to protect the brand image and related commercial interests. This research develops a trademark similarity assessment methodology based on the US trademark law related to the high likelihood of confusion and associated regulations. The research focuses on identifying trade mark image similarity using a deep learning approach. The convolutional neural network (CNN) and Siamese neural network (SNN) algorithms are modeled and trained using Cifar-10 and TopLogo-10 corpuses. These corpuses consist of more than 100,000 positive image pairs and more than 150,000 negative image pairs as training data. After training the model, an image input to the model extracts and recommends similar trade mark images found in the corpus. The solution assists users registering new trademarks to identifying similar marks that may lead to disputes. The solution also automatically screens images to identify marks that potentially infringe upon registered trademarks.
Using Machine Learning Approach to Identify Synonyms for Document Mining
(2019-01-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Wu, Jheng-Long
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Tsai, Kevin T. -C
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管理科學系
Technical or knowledge documents, such as research papers, patents, and technical documents, e.g., request for quotations (RFQ), are important knowledge references for multiple purposes. For example, enterprises and R&D institutions often need to conduct literature and patent searches and analyses before, during, and after R&D and commercialization. These knowledge discovery processes help them identify prior arts related to the current R&D efforts to avoid duplicating research efforts or infringing upon existing intellectual property rights (IPRs). It is common to have many synonyms (i.e., words and phrases with near-identical meanings) appeared in documents, which may hinder search results, if queries do not consider these synonyms. For instance, conducting "freedom-to-operate" (FTO) patent search may not find all related patents if synonyms were not taking into consideration. This research develops methodologies of generating domain specific "word" and "phrase" synonym dictionaries using machine learning. The generation and validation of both domain-specific "word" and "phrase" synonym dictionaries are conducted using more than 2000 solar power related patents as testing document set. The testing result shows that, in the solar power domain, both word level and phrase level dictionaries identify synonyms effectively and, thus, significantly improve the patent search results.
Behavioral Therapy for Phobias Using Immersive Virtual Reality Technology
(2020-04-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Chang, Chia-Ming
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Govindarajan, Usharani Hareesh
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Shih, Xyuan-Yu
Mental disorders are difficult to diagnose and treat. Global economic growth, changing social organizations, and urban conditions affect mental health resulting in increased costs, limited benefits, and fewer facilities for behavioral treatment. Virtual reality-based behavioral treatments for mental illness offer benefits to supplement conventional therapies. Virtual Reality Treatment (VRT) let patients confront phobias using a virtual environment with a therapist. A therapist observes and coaches the patient through increasingly difficult levels of confrontation to desensitize patient phobias. This research presents a generic framework and discusses the related behavioral therapy. A VRT for confronting fear of spiders is the case used for the technology development and data collection processes which establish the basis for future clinical trials and analysis. The preliminary trial of the arachnophobia VRT platform with eight scenario levels has produced accurate bio-feedback results. The system demonstrates promise for rapid VRT application development for clinical behavioral psychotherapy.
Intelligent compilation of patent summaries using machine learning and natural language processing techniques
(2020-01-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Wu, Jheng-Long
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Wang, Jack W. C.
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管理科學系
Patents are a type of intellectual property with ownership and monopolistic rights that are publicly accessible published documents, often with illustrations, registered by governments and international organizations. The registration allows people familiar with the domain to understand how to re-create the new and useful invention but restricts the manufacturing unless the owner licenses or enters into a legal agreement to sell ownership of the patent. Patents reward the costly research and development efforts of inventors while spreading new knowledge and accelerating innovation. This research uses artificial intelligence natural language processing, deep learning techniques and machine learning algorithms to extract the essential knowledge of patent documents within a given domain as a means to evaluate their worth and technical advantage. Manual patent abstraction is a time consuming, labor intensive, and subjective process which becomes cost and outcome ineffective as the size of the patent knowledge domain increases. This research develops an intelligent patent summarization methodology using artificial intelligence machine learning approaches to allow patent domains of extremely large sizes to be effectively and objectively summarized, especially for cases where the cost and time requirements of manual summarization is infeasible. The system learns to automatically summarize patent documents with natural language texts for any given technical domain. The machine learning solution identifies technical key terminologies (words, phrases, and sentences) in the context of the semantic relationships among training patents and corresponding summaries as the core of the summarization system. To ensure the high performance of the proposed methodology, ROUGE metrics are used to evaluate precision, recall, accuracy, and consistency of knowledge generated by the summarization system. The Smart machinery technologies domain, under the sub-domains of control intelligence, sensor intelligence and intelligent decision-making provide the case studies for the patent summarization system training. The cases use 1708 training pairs of patents and summaries while testing uses 30 randomly selected patents. The case implementation and verification have shown the summary reports achieve 90% and 84% average precision and recall ratios respectively.
E-discover State-of-the-art Research Trends of Deep Learning for Computer Vision
(2019-01-01)
Li, Annie A. S.
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Trappey, Amy J. C.
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Trappey, Charles V.
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Fan, C. Y.
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交大名義發表
Computer vision (CV) attempts to mimic human eyes for image processing and identifications of detailed visual information, such as object positions, features of appearances, and even human emotions and behaviors. In this research, more than one hundred literatures, relating to applying deep learning (DL) methodologies in advanced computer visions (2010 similar to 2018), are reviewed and analyzed. The objective is to discover the state-of-the-art DL methods, topics, and trends for CV and their practical applications. DL algorithms aim at representing multilevels of distributed neural networks. Because of the enhancement of high speed computational power, DL modeling, based on accumulated big data analytics, has found practical applications for non-supervised intelligent decision supports, such as detection of product defects and prognosis of machine malfunctions based on real-time signal or feature data analyses. There are a vast number of literature, describing DL related researches, developments, and implementations for problem solving. For the comprehensive mining of the related literature, we integrate Latent Dirichlet Allocation (LDA), K-means (Clustering), and normalized term frequency-inverse document frequency (NTF-IDF) approaches to discover, or called technology mining, of the major trends in DL for computer visions, specifically for key applications in object detection, semantic segmentation, image retrieval, and human pose estimation.
Patent Analytics of Robotics Technology for Intelligent Manufacturing in the Semiconductor Industry
(2018-01-01)
Chen, Paul P. J.
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Trappey, Amy J. C.
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Lin, Betty H. L.
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Trappey, Charles V.
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管理科學系
Manufacturers are making a transition to intelligent production, particularly in high-tech industries. Robotic arms play a crucial role in the effectiveness of transforming processes. This research searches and analyzes patents related to robotic arms utilized by intelligent manufacturing systems. For this preliminary study, global patent trends are discussed which help evaluate the technology domain to be developed. Key words from a literature review are used to search for relevant patents, and then the patent text is analyzed using text mining software and program. The core technique applied to the patent document analysis is "Term Frequency-Inverse Document Frequency" (TF-IDF). Using TF-IDF, the patent documents are grouped into meaningful sub-technology cluster. Thus, the economic data related to the semiconductor industry is analyzed to correlate the economic impact which may be related to the transitions in technology. The data of Taiwan firms were targeted for anticipating research directions and expand intellectual property domains to invest in and pursue. For the advanced analysis, the international patent codes for global and local companies are compared. The final results help define the strength and weakness of Taiwanese robotic technology development for the semiconductor industry. Patent cluster and patent distribution analysis via a technology function matrix are the primary research approaches used for the case study. The research prepares for the development of a theoretical foundation to predict intelligent manufacturing trends.
Advances in aligning knowledge systems, improving business logistics, driving innovation and adapting customer centric services
(2010-11-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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管理科學系
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Department of Management Science
An XML based supply chain integration hub for green product lifecycle management
(2010-11-01)
Taghaboni-Dutta, Fataneh
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Trappey, Amy J. C.
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Trappey, Charles V.
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管理科學系
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Department of Management Science
The emergence of new regulations for the disposal of electrical and electronic equipment, directives on the use of certain hazardous substances, and the Kyoto Protocol are changing industries approach to product design and manufacturing. Traditionally, product lifecycle management systems provide a platform for the management of data related to the creation and disposal of products. These systems assist the participants of the products' life cycle processes (manufacturers, suppliers, customers, and regulators) to use data efficiently for planning and control. However, most data collected do not resolve the environmental issues which arise when selling the products or arranging the products to be replaced and disposed. In this paper, we present an Integrated Green Parts Information Platform (IGPIP) framework (The frame work revisited here was published as "A Framework for a Green Product Lifecycle Management System", Issues in Information Systems, Vol. IX, No. (2), 2008, 123-131.) and a working prototype of the system. This system uses XML file transmission to improve the quality, cost and time-to market issues for green designs. (C) 2010 Elsevier Ltd. All rights reserved.
Computer Supported Technology Function Matrix Construction for Patent Data Analytics
(2017-01-01)
Jhuang, Allen C. C.
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Trappey, Charles V.
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Sun, John J. H.
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Govindarajan, Usharani Hareesh
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Trappey, Amy J. C.
Patent analysis helps companies understand their intellectual property (IP) portfolio and develop competitive marketing and management strategies. A Technology Function Matrix (TFM) is a critical approach for patent data analytics. This paper develops a generic computer supported TFM construction methodology that can be used for creating patent technical maps for any given domain. The approach is adopted for the case of the Internet of Things (loT) patent technology analysis in the context of Industry 4.0 111. The aim of this article is to provide the methodology and analysis methods for loT patent TFM and introduce computer supported IP and patent knowledge e -discovery.

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