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An Implementation of Trust Chain Framework with Hierarchical Content Identifier Mechanism by Using Blockchain Technology
No full text[[abstract]]Advances in information technology (IT) and operation technology (OT) accelerate the development of manufacturing systems (MS) consisting of integrated circuits (ICs), modules, and systems, toward Industry 4.0. However, the existing MS does not support comprehensive identity forensics for the whole system, limiting its ability to adapt to equipment authentication difficulties. Furthermore, the development of trust imposed during their crosswise collaborations with suppliers and other manufacturers in the supply chain is poorly maintained. In this paper, a trust chain framework with a comprehensive identification mechanism is implemented for the designed MS system, which is based and created on the private blockchain in conjunction with decentralized database systems to boost the flexibility, traceability, and identification of the IC-module-system. Practical implementations are developed using a functional prototype. First, the decentralized application (DApp) and the smart contracts are proposed for constructing the new trust chain under the proposed comprehensive identification mechanism by using blockchain technology. In addition, the blockchain addresses of IC, module, and system are automatically registered to InterPlanetary File System (IPFS), individually. In addition, their corresponding hierarchical CID (content identifier) values are organized by using Merkle DAG (Directed Acyclic Graph), which is employed via the hierarchical content identifier mechanism (HCIDM) proposed in this paper. Based on insights obtained from this analysis, the trust chain based on HCIDM can be applied to any MS system, for example, this trust chain could be used to prevent the counterfeit modules and ICs employed in the monitoring system of a semiconductor factory environment. The evaluation results show that the proposed scheme could work in practice under the much lower costs, compared to the public blockchain, with a total cost of 0.002094 Ether. Finally, this research is developed an innovation trust chain mechanism that could be provided the system-level security for any MS toward Industrial 4.0 in order to meet the requirements of both manufacturing innovation and product innovation in Sustainable Development Goals (SDGs)
Blockchain Assisted Secure Data Sharing Model for Internet of Things Based Smart Industries
No full text[[abstract]]Industrial Internet of Things is focused to improve the performance of smart factories through automation and scalable functions. IoT paradigm, information and communication technology, and intelligent computing are assimilated as a single entity for industrial automation, optimization, sharing and security, and scalability. In a view of the security requirement in smart industry data sharing through IoT, this article introduces a blockchain-assisted secure data sharing (BSDS) model. This model is responsible for administering inbound and outbound security in data acquisition and dissemination. The inbound acquisition is first classified using recurrent learning to identify adverse sequences in data dissemination. In the outbound security measure, end-to-end authentication based on the blockchain information of reputation and sequence differentiation is engaged. The blockchain paradigm controls the data gathering and dissemination instances through the classification and integrity verification in both the industry and processing terminals. For this purpose, the functions of the blockchain are riven for data gathering and monitoring in the smart industry whereas integrity and sequence verification is performed by the nonmining blockchain terminal in the processing environment. The integrated security measures are capable of maximizing the response rate by confining false alarm progression, failure rate, and time delay. Statistical analysis shows that the BSDS achieves a 5.67% high response rate and reduces the failure rate by 2.14%. Further, it achieves 3.12%, maximizes response rate by 6.63%, and reduces delay by 11.91%, respectively
Boosting-based DDoS Detection in Internet of Things Systems
No full text[[abstract]]Distributed Denial-of-Service (DDoS) attacks remain challenging to mitigate in the existing systems, including in-home networks that comprise different Internet of Things (IoT) devices. In this article, we present a DDoS traffic detection model that uses a boosting method of logistic model trees for different IoT device classes. Specifically, a different version of the model will be generated and applied for each device class since the characteristics of the network traffic from each device class may have subtle variation(s). As a case study, we explain how devices in a typical smart home environment can be categorized into four different classes (and in our context, Class 1—very high level of traffic predictability, Class 2—high level of traffic predictability, Class 3—medium level of traffic predictability, and Class 4—low level of traffic predictability). Findings from our evaluations show that the accuracy of our proposed approach is between 99.92% and 99.99% for these four device classes. In other words, we demonstrate that we can use device classes to help us more effectively detect DDoS traffic
PCNNCEC: Efficient and Privacy-Preserving Convolutional Neural Network Inference Based on Cloud-Edge-Client Collaboration
No full text[[abstract]]Deploying convolutional neural network (CNN) inference on resource-constrained devices remains a remarkable challenge for industrial Internet of Things (IIoT). Although the cloud computing shows great promise in machine learning training and prediction, outsourcing data to remote cloud always incurs privacy risk and high latency. Therefore, we design a new framework for efficient and privacy-preserving CNN inference based on cloud-edge-client collaboration (namedPCNNCEC). In PCNNCEC, the model of cloud and the data of client in IIoT are split into two shares and sent to two non-colluded edge servers. By applying the arithmetic secret sharing and pre-computation of beaver's triplets, the two edge servers can jointly calculate the predicting results without learning anything about the model and data. To speed up the pre-computation of offline phase and not sacrifice security, the task of triplets generation is delegated to the cloud, so that the edge servers do not require frequent interactions to generate triplets themselves or introducing additional trusted party. The experimental results show the proposed private comparison protocol achieves a better tradeoff between low latency and high throughput, when it is compared with garbled circuit based protocols and other secret sharing based protocols. Additionally, the benchmarks conducted on realistic MNIST and CIFAR-10 datasets demonstrate that PCNNCEC costs less communication and runtime than two recently related schemes under the same security level
PPRP: Preserving-Privacy Route Planning Scheme in VANETs
No full text[[abstract]]Route planning helps a vehicle to share a message with the roadside units (RSUs) on its path in advance, which greatly speeds the authentication between the vehicle and the RSUs when the vehicle enters the RSUs’ coverage. In addition, since only a small amount of necessary information needs to be shared between the vehicle and the RSUs, route planning can reduce the storage overhead of the vehicle’s on-board unit (OBU) and the RSUs. However, the message sharing requires the assistance of the certification authority (CA), which will lead CA easily to obtain the vehicle’s planning route. Although CA knows the vehicle’s registration information and helps the vehicle to communicate with RSUs, it is unacceptable that the path of their vehicle is obtained by CA for most drivers. In fact, vehicle’s sensitive information such as planning route, starting time, stop place, should be privacy for others including CA. Inspired with the method of oblivious transfer, a preserving-privacy route planning scheme in VANETs is proposed in this article, in which, a vehicle deduces the information of RSUs on its path with the help of CA, while CA knows nothing about which RSUs’ information has been deduced by the vehicle. Later, fast authentication or other service is easily achieved between the vehicle and the RSUs (V2R) with the pre-shared information. After V2R authentication, vehicles could easily communicate with adjacent vehicles with the help of RSUs (V2V). Finally, compared with related schemes, performance evaluation illustrates the proposed scheme is better in terms of time consumption
Regulated Two-Dimension Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid
No full text[[abstract]]Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification
A New Technique for Converting Normally-on AlGaN/GaN Transistor into Normally off mode Using TCAD Simulation.
No full text[[abstract]]工业技术硕士论文 半导体技术研发硕士项目
由于自发极化和压电极化的存在，GaN 通常是一种常开器件。因此，我们对增强型器件进行了氮离子注入，使其正常关闭。
本研究提供了一种新的、可大规模制造的低成本离子注入方法，用于将 D 型 AlGaN/GaN 晶体管转变为 E 型。我们使用 TCAD 模拟在 300 KeV 能量下使用氮注入将 D 模式校准晶体管转换为常闭器件，这使带隙能量高于费米水平此外，使用不同的氮注入剂量获得改变的阈值电压 (Vth) .由于碰撞电离，在室温下，E 型 AlGaN/GaN HEMT 显示出 127.4 的击穿电压，具有正温度系数 (3 × 10−3 K −1)。当前工作中的 GaN 器件 E-Mode AlGaN 显示了 45.3% 的漏极电流平均增益，这支持了传输特性的研究。因此，建议的方法允许对具有 1487 cm2V-1s-1 迁移率和 4 V 时 1.4 A/mm 漏极饱和电流的 100μm 宽器件进行建模。建议方法在性能和性能方面的优势处理总结在这里。[[abstract]]Thesis for the Degree of Master of Science in Industrial Technology R&D Master Program on Semiconductor Technology
Because of the presence of spontaneous and piezoelectric polarizations, GaN is a normally on device in general. So, we used Nitrogen ion implantation for the Enhancement mode device to turn it off normally.
This study provides a new, mass-manufacturable, low-cost ion implantation approach for transforming D-Mode AlGaN/GaN transistors into E-mode. We transformed a D-Mode calibrated transistor into a normally off device using nitrogen implantation at a 300 KeV energy, which gave bandgap energy above the Fermi level, utilizing TCAD simulations Furthermore, the altered threshold voltage (Vth) was obtained using varying nitrogen implantation dosage. Because of impact ionization, at room temperature the E-mode AlGaN/GaN HEMT displayed 127.4 breakdown voltage, with the positive temperature coefficient (3 × 10−3 K −1). The average gain of drain current with 45.3% was shown by E-Mode AlGaN, GaN device in the current work, which supports an investigation of transfer characteristics. As a result, the suggested approach permitted the modelling of 100μm-wide device with the mobility of 1487 cm2V-1s-1 and the drain saturation current of 1.4 A/mm at 4 V. The benefits for the suggested approach in terms of performance and processing are summarized here
A Study of the Effect of Service Quality on Customer Satisfaction in Taichung Department Store
No full text[[abstract]]本研究探討顧客至百貨公司消費，對於無形的服務品質對顧客消費行為之影響，透過量化資料研究來找出與之相關性最大之因素，並藉此找出顧客對於哪些原因會影響其購買意願，進而使百貨公司了解如何透過服務品質提升整體收益。
本研究共發放233份資料，收回 233 筆有效問卷。研究發現，顧客對於服務品質會隨著特定因素而改變，如:顧客會對百貨公司的要求會隨著去的次數越來越高、將居家生活用品及嬰幼兒用品設立於相近樓層方便有家庭的顧客購物、百貨公司是否有針對不同年齡層及消費水平舉辦相應的優惠或活動亦或是百貨公司離顧客自己家的距離會影響前往購物的次數及意願...等等，而這些因素都是此報告著重探討及分析的要點。本研究還將依不同結果給予不同建議及方向提供參考。[[abstract]]This study investigates the impact of intangible service quality on customers' consumption behavior when they visit department stores, and identifies the most relevant factors through quantitative data research.
A total of 233 questionnaires were distributed and 233 valid questionnaires were returned. The study found that the quality of services provided by customers changed according to specific factors, such as: customers' demand for department stores increased with the number of visits, the convenience of having household products and baby products on the same floor for customers with families, whether the department stores organized special offers or activities for different age groups and consumption levels, or the distance of the department stores from customers' own homes affected the frequency and willingness to shop. shopping frequency and willingness... These factors are the main focus of this report. The study will also provide different recommendations and directions based on the results
綠色產業下的永續經濟創生研究(1/3)
No full text[[abstract]]本計畫以雲林縣現有的整體發展目標與在地現況進行實踐，以「綠色產業下的永續經濟創生研究」為主題，具體研究與實踐案例包含推動地方創生、數位科技應用、資源整合運用等面向，分為三個子計畫：一、數位轉型，以數位化轉型角度進行縣政推動之數位轉型導入，建立永續能源數位地圖與農電共生數位化平台並整合IoT技術提升綠能下經濟產值。二、整合實踐，透過在地方之場域的田野調查，進行公私協力合作之內容研究藉以得出可能之永續、可持續性發展之對策。三、迴歸平台，透過評估雲林縣建構勞務人力調度平臺之可行性，對於區域內產業勞動力需求、各鄉鎮人力分組相互調度及可能性，建立青年回鄉就業迴歸平台，提供產業、地方文化、人文素養等資源。 [[note]]科技部[[note]]2022-08-01～2023-07-3