The globalized information-sharing phenomena facilitated by technologies such as the Internet have increased the demand for translation services. Automating translation has been at the forefront of solutions to addres...
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ISBN:
(数字)9789819735594
ISBN:
(纸本)9789819735587;9789819735594
The globalized information-sharing phenomena facilitated by technologies such as the Internet have increased the demand for translation services. Automating translation has been at the forefront of solutions to address the demand. Automatic translation services have been available for sometimes provided by tech companies such as Google;however, achieving full translation accuracy is an ongoing challenge. In this paper, a fuzzy logic-based evaluation metric is proposed for evaluating machine translation accuracy. Evaluation results generated by the metric is compared with evaluation results generated by the bilingual evaluation understudy (BLEU) which is one of the most widely used machine translation accuracy evaluation metrics. The accuracy of evaluation results produced by both metrics are benchmarked against human-based translation accuracy evaluations for over a set of sentences translated from Turkish to English by tools Google translation, Yandex translation, and a simple neural machine translation prototype developed by the authors. The results show that the proposed fuzzy logic-based metric evaluates the accuracy of machine translations more effectively than the BLEU metric.
This paper proposes the hardware and software architecture approach to fulfill the requirements of smart and intelligent building automation in current trend of construction technology. This paper is concerned with mo...
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ISBN:
(纸本)9789819713257;9789819713264
This paper proposes the hardware and software architecture approach to fulfill the requirements of smart and intelligent building automation in current trend of construction technology. This paper is concerned with monitoring and controlling of various modules used in the building automation system (BAS). BAS is attractive to facility manager and popular, because it helps in operational effectiveness of the building. Hence BAS must be optimized, well-designed, and well implemented. A measurement tool in the form of a performance index is used to improve the level of automation in a building. The aim of this paper is to describe the implementation of BAS using ARM controllers and wired and wireless nodes to develop building control application. Communication between main control station (server) and nodes (controllers, sensors, actuators) is established using wired channel (RS-485) and wireless channel (ZigBee). The device connected to RS-485 bus can communicate in half duplex mode with any other devices. The ZigBee is used for low cost, low power, and for short range communication. The security feature can be improved by using different access modes like developer and user mode. Therefore the above mentioned system (BAS) effectively monitors and controls different modules/nodes on single platform. The hardware and software architecture proposed plays vital role in implementation of BAS to address the quality issues like platform dependency, complexity, flexibility, security, power, etc.
This article presents a method for synthesizing a robot manipulator's adaptive sliding mode controller based on a neural network. In actual working conditions, the robot's dynamic equation has strong nonlinear...
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ISBN:
(纸本)9783031762314;9783031762321
This article presents a method for synthesizing a robot manipulator's adaptive sliding mode controller based on a neural network. In actual working conditions, the robot's dynamic equation has strong nonlinearity, the parameters change uncertainly, and in many cases, the robot is affected by unmeasured external disturbances. Using an RBF neuron network and adaptive control, we propose a solution to approximate and compensate for the uncertain components and external disturbances. The robust control term based on sliding mode control is designed to overcome approximation errors with chattering in the control signal reduced to a minimum. The simulation outcomes indicate that the robot controller suggested in this article possesses high quality, adaptability, and robust resistance to interference.
This paper presents an aggressive trajectory tracking method for a small lightweight nano-quadrotor using nonlinear model predictive control (NMPC) based on acados. Controlling a nano quadrotor for accurate trajectory...
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ISBN:
(数字)9783031448515
ISBN:
(纸本)9783031448508;9783031448515
This paper presents an aggressive trajectory tracking method for a small lightweight nano-quadrotor using nonlinear model predictive control (NMPC) based on acados. Controlling a nano quadrotor for accurate trajectory tracking at high speed in dynamic environments is challenging due to complex aerodynamic forces that introduce significant disturbances and large positional tracking errors. These aerodynamic effects are difficult to be identified and require feedback control that compensates for them in real time. NMPC allows the nano-quadrotor to control its motion in real time based on onboard sensor measurements, making it well-suited for tasks such as aggressive maneuvers and navigation in complex and dynamic environments. The software package acadosenables the implementation of the NMPC algorithm on embedded systems, which is particularly important for nano-quadrotor due to its limited computational resources. Our autonomous navigation system is developed based on an AI-deck that is a GAP8-based parallel ultra-low power computing platform with onboard sensors of a multi-ranger deck and a flow deck. The proposed method of NMPC-based trajectory tracking control is tested in simulation and the results demonstrate its effectiveness in trajectory tracking while considering the dynamic environments. It is also tested on a real nano quadrotor hardware, 27-g Crazyflie 2.1, with a customized MCU running embedded NMPC, in which accurate trajectory tracking results are achieved in dynamic real-world environments.
Blockchain technology has affected numerous sectors, and one of its most potential uses is supply chain management. Traditional supply chains frequently suffer from inefficiencies, a lack of transparency, and fraud vu...
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ISBN:
(纸本)9789819735617;9789819735624
Blockchain technology has affected numerous sectors, and one of its most potential uses is supply chain management. Traditional supply chains frequently suffer from inefficiencies, a lack of transparency, and fraud vulnerability. These problems are addressed by the decentralized, immutable ledger that blockchain technology delivers, the platform for controlling the movement of products and information. This study aims to thoroughly examine both the possible advantages and difficulties of applying blockchain technology to supply chain management. It starts by giving a summary of the fundamental ideas and characteristics of blockchain technology, emphasizing how it can fundamentally alter the way supply chains function. This study looks at the difficulties and restrictions that businesses may have while implementing blockchain technology, including scalability constraints, legal obstacles, and interoperability problems. The necessity of standardized frameworks and the value of stakeholder participation are also discussed to realize the full. Blockchain technology in supply chain management has a lot of potential to boost efficiency, transparency, and traceability across a range of sectors. This article seeks to offer insights and advice to firms contemplating the implementation of blockchain to alter their supply chain operations by examining the technology's advantages and disadvantages.
Through a comprehensive study of the literature, this paper examines the fundamentals of digital resilience and the contribution of the Project Management Office (PMO) and adaptive capacity within organizations. The m...
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ISBN:
(数字)9783031456510
ISBN:
(纸本)9783031456503;9783031456510
Through a comprehensive study of the literature, this paper examines the fundamentals of digital resilience and the contribution of the Project Management Office (PMO) and adaptive capacity within organizations. The main objective was to identify the main dimensions, proximity, and convergence between project management and information generating center (PMO), information systems, and digital resilience. In addition, a bibliometric study was performed to identify the co-occurrences, dimensions, and cluster analysis. The results are presented in this article, and where 4 clusters were identified and at the end a conceptual framework is presented.
Recently, point cloud semantic segmentation has played an important role in real-world applications such as autonomous driving and robotics. In this context, while recognized as an efficient semantic segmentation mode...
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ISBN:
(纸本)9783031761966;9783031761973
Recently, point cloud semantic segmentation has played an important role in real-world applications such as autonomous driving and robotics. In this context, while recognized as an efficient semantic segmentation model with a good balance between performance and complexity, SqueezeSegv2 is still too heavy for resource-constrained devices. In this paper, we propose Lite-GrSeg, a compact and effective semantic segmentation model inspired by SqueezeSegv2. Lite-GrSeg adopts a cutting-edge design architecture that leverages SqueezeSegV2 with group convolution and spatial separable convolution to reduce the model's complexity. Additionally, Lite-GrSeg introduces a novel structure called the Spatial Context Aggregation Module (Spatial-CAM) to enhance the model's discriminability. Through the simulations benchmarked on the PandaSet dataset, Lite-GrSeg significantly reduces computational complexity and model size while presenting competitive segmentation accuracy compared to SqueezeSegV2, thus making it a compelling choice for lightweight applications and opening up exciting possibilities for development on resource-constrained IoT devices.
Convolutional Neural networks (CNNs) have demonstrated remarkable performance in various image classification tasks, including the classification of handwritten digits in the MNIST dataset. It is evident that it has a...
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ISBN:
(纸本)9783031669644;9783031669651
Convolutional Neural networks (CNNs) have demonstrated remarkable performance in various image classification tasks, including the classification of handwritten digits in the MNIST dataset. It is evident that it has a vast go, but it too faces certain limitations such as overconfidence, complexity and overfitting. Hence, an approach leveraging Regularized Conditional Entropy (RCE) as loss function within a CNN architecture for enhanced accuracy in digit classification as a whole called Convolutional Neural Network using Regularized Conditional Entropy Loss (CNNRCoE). The regularization technique employed aims to mitigate overfitting and improve generalization by penalizing the complexity of the model. In this study, extensive experiments have been conducted on the MNIST Handwritten digits inclusive of 5 datasets to evaluate the effectiveness of the proposed method, comparing it with traditional CNN architectures. The results demonstrate that the integration of RCoE within the CNN framework yields superior performance, achieving state-of-the-art accuracy at about 98% with the increase about 20%, while maintaining sturdiness against overfitting.
Accessing data is difficult and time-consuming when developing solutions for human activity recognition (HAR). Additionally, personal data makes it challenging to use for research purposes. Nevertheless, recent advanc...
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ISBN:
(数字)9783031775710
ISBN:
(纸本)9783031775703;9783031775710
Accessing data is difficult and time-consuming when developing solutions for human activity recognition (HAR). Additionally, personal data makes it challenging to use for research purposes. Nevertheless, recent advancements in synthetic data generation techniques offer an opportunity to address these issues. Whilst obtaining HAR data is essential, it is equally crucial that the data meets the high standards of quality, utility and fidelity. To date, no research has been conducted to understand what impact the proportion of real and synthetic data has on the performance of the HAR models. This research focuses on a comprehensive analysis of the distribution and performance of generated datasets when applied to various machine learning models. We systematically create training datasets with various proportions of real and synthetic data and assess their impact on performance of HAR systems. Our analysis employs common machine learning models such as Decision Tree (DT), Gaussian Naive Bayes (GNB), Support Vector Machines (SVM), Linear Support Vector Machine (L-SVM), Random Forest (RF), Gradient Boosting (GB) and Shallow Neural networks (SNN). By evaluating the models on various proportions of real and synthetic data for training, we observed that increasing the proportion of synthetic data in the training set had the impact of improving the model's performance on unseen instances. Specifically, we achieved 0.970 accuracy by boosting the real training dataset by 90% using synthetic data in a RF model on 5-fold cross-validation. Furthermore, we aim to understand the trade-offs and benefits associated with each approach. This study aims to provide insights into the viability of synthetic data for HAR tasks and establish guidelines for its effective use. Ultimately, our goal is to contribute to developing more effective HAR models by analysing the performance of different machine learning techniques on both real and synthetic data. In the future, we plan to extend our work
Since 2002, there has been a significant increase in the study of work-life balance by researchers, resulting in a growing body of research findings. It is imperative for employees to not only exhibit optimal performa...
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ISBN:
(纸本)9789819713196;9789819713202
Since 2002, there has been a significant increase in the study of work-life balance by researchers, resulting in a growing body of research findings. It is imperative for employees to not only exhibit optimal performance within the workplace, but also have a well-rounded personal life encompassing familial and communal engagements. In order to maintain the employment of their human resources, organizations must diligently consider the capacity of employees to effectively integrate their professional and personal spheres. A significant number of individuals who are employed express a prevailing sentiment that the equilibrium between professional obligations and personal life is lacking inside the organizational setting. The objective of this research is to assess the progress made in the field of work-life balance. This study aims to provide readers with a full understanding of the current dynamics and shed light on the current status of the literature pertaining to work-life balance. The significance of this research is in examining the inclination of prior researchers towards the investigation of the theory of work-life balance. Furthermore, this study offers a comprehensive examination of the existing body of literature on work-life balance through the utilization of bibliometric analysis. The Scopus database was utilized for data collecting in this study. The database utilized for this research consisted of a total of 1003 documents spanning the years 2002 to 2023. This study aims to provide a comprehensive analysis of the existing body of research on work-life balance. Specifically, it will examine the theoretical foundations, publication patterns, and citation trends within this field. Additionally, the study will investigate commonly referenced journals, frequently employed research keywords, and the formation of research clusters. Based on an analysis conducted using Scopus, it has been determined that the domains of management and business research in the Un
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