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IEEE International Conference on Systems, Man and Cybernetics

作者： Takashi Kuwahara Itsuki Takahashi Shintaro Harikae Ph.D. Program in Empowerment Informatics School of Integrative and Global Majors University of Tsukuba Ibaraki Japan Independent Ibaraki Japan Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki Japan

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

In snowboard training, it is especially important to learn proper weight transfer and to perceive a gap between the perceived movement and the actual body movement. However, previous training systems don't have sufficient function, and the effectiveness of each feedback on weight transfer, which includes not only the front and back but also left and right has not been reported. In this study, we propose and develop a real-time snowboard training system for a novice using visual and auditory feedback, and confirm its basic performance and effectiveness of each feedback. This system mainly consists of four components: (1) a center of pressure (COP) detection device, (2) a posture estimation device, (3) a visual feedback device, (4) an auditory feedback device. We experimented to confirm the feasibility to enhance motor learning by using the developed system and to evaluate the effectiveness of each feedback on weight transfer, which includes not only the front and back but also left and right. In conclusion, we developed a real-time snowboard training system for a novice using visual and auditory feedback and confirmed that our system could assist the proper weight transfer. Furthermore, in short-term learning, we confirmed that the effectiveness of each feedback, and visual feedback or visual and auditory feedback is the best way to enhance motor learning.

关键词： Training Visualization Protocols Snow Real-time systems Floors Sports

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Development of knowledge management system for determining organizational culture in micro, small and medium enterprises using organizational culture assessment instrument 1

Development of knowledge management system for determining o...

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2017 1st International Conference on engineering and Applied Technology, ICEAT 2017

作者： Aziz, R.Z.A. Azima, M.F. Irianto, S.Y. Informatics Engineering Department Graduate Program Informatics and Business Institute Darmajaya Bandar Lampung Indonesia

Organizations must improve their competitiveness due to global competition and free market. Organizational culture and continuous improvement are one of the main factors used not only to face the competitiveness in the free market but also to improve organizational performance and vision and mission achievement. Moreover, the organizational culture also has an important element for determining the direction of Micro, Small and Medium Enterprises (MSMEs). However, many MSMEs currently do not pay attention to their organizational culture so that difficulties emerge in determining the right direction and policy. To solve the problems, this research aimed to determine the companies' organizational culture by using organizational culture assessment instrument (OCAI) based on knowledge management system (KMS) in order to provide recommendations. In the developing the system, the researchers conducted surveys to several MSMEs and invite experts and practitioners to conduct the process of knowledge capture and literature study and to achieve the best organizational culture recommendations on MSMEs. This research produced a software used for determining the organizational culture based on KMS and OCAI principles. The developed software was tested to several companies and MSMEs to obtain results with a very high level of accuracy in providing recommendations. © Published under licence by IOP Publishing Ltd.

关键词： Competition

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Hybrid Intelligent-Annotation Organ Segmentation on Medical Datasets

Hybrid Intelligent-Annotation Organ Segmentation on Medical ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tao Peng Jing Zhao Yidong Gu Gongye Di Lei Zhang Jing Cai School of Future Science and Engineering Soochow University Suzhou China Department of Radiation Oncology UT Southwestern Medical Center Dallas TX USA Department of Ultrasound Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital Beijing China Department of Medical Ultrasound Suzhou Municipal Hospital The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Jiangsu China Department of Ultrasonic The Affiliated Taizhou People's Hospital of Nanjing Medical University Taizhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan Jiangsu China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China

Ultrasound image segmentation is crucial for early disease detection and treatment planning but remains a challenging task due to the low contrast of organ boundaries and varying image quality. Current methods often require manual intervention or have limited accuracy. In this paper, we propose a novel hybrid framework that combines an automatic option polygon segment (AOPS) algorithm and a distributed- and memory-based evolution (DME) algorithm for precise ultrasound organ segmentation. Our pipeline consists of two cascaded stages: (1) a coarse segmentation step using the AOPS algorithm, which determines the number of vertices/clusters without human intervention, and (2) a refinement step using the DME algorithm to hunt for the optimal neural network, which is then used to represent a smooth, explainable mathematical expression of the organ boundary. We employ the fractional backpropagation learning network with L2 regularization (FBLN) for training and use the scaled exponential linear unit (SELU) activation function to address the vanishing gradient problem. This is a new attempt such a hybrid framework is applied to ultrasound organ segmentation tasks, and it demonstrates significant contributions in terms of accuracy, smoothness, and computational efficiency.

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Contour Detection from Ultrasound Kidney Images with A Coarse-to-Fine Approach

Contour Detection from Ultrasound Kidney Images with A Coars...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tao Peng Yidong Gu Yanqing Xu Caishan Wang Lei Zhang Jing Cai School of Future Science and Engineering Soochow University Suzhou China Department of Radiation Oncology UT Southwestern Medical Center Dallas TX USA Department of Medical Ultrasound the Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Municipal Hospital Suzhou Jiangsu China McDermott Center for Human Growth and Development University of Texas Southwestern Medical Center Dallas Texas USA Department of Ultrasound the Second Affiliated Hospital of Soochow University Suzhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan Jiangsu China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China

Ultrasound kidney image segmentation presents significant challenges due to missing or ambiguous boundaries. In this study, we introduce a coarse-to-refinement approach incorporating four novel aspects. Firstly, we leverage the properties of a principal curve (PC) to automatically fine-tune the curve shape and employ a neural network's learning ability to reduce model error. Secondly, a deep fusion learning network is utilized for the coarse segmentation step, incorporating a parallel architecture to enhance deep-learning performance. Thirdly, addressing the limitation of standard PC-based methods in determining the number of vertices automatically, we propose an automatic searching polygon tracking method using a mean shift clustering-based approach to replace the projection and vertex extension step in standard PC-based methods. Lastly, we develop an explainable mathematical map function for the kidney contour, as denoted by the neural network output (i.e., optimized vertices), which aligns well with the ground truth contour. We conducted various experiments to evaluate our method's performance, demonstrating its effectiveness in ultrasound kidney image segmentation.

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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

arXiv

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arXiv 2020年

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil & Environmental Engineering University of Iowa Iowa City United States

—LIDAR (light detection and ranging) is an optical remote-sensing technique that measures the distance between sensor and object, and the reflected energy from the object. Over the years, LIDAR data has been used as the primary source of Digital Elevation Models (DEMs). DEMs have been used in a variety of applications like road extraction, hydrological modeling, flood mapping, and surface analysis. A number of studies in flooding suggest the usage of high- resolution DEMs as inputs in the applications improve the overall reliability and accuracy. Despite the importance of high-resolution DEM, many areas in the United States and the world do not have access to high-resolution DEM due to technological limitations or the cost of the data collection. With recent development in Graphical Processing Units (GPU) and novel algorithms, deep learning techniques have become attractive to researchers for their performance in learning features from high-resolution datasets. Numerous new methods have been proposed such as Generative Adversarial Networks (GANs) to create intelligent models that correct and augment large-scale datasets. In this paper, a GAN based model is developed and evaluated, inspired by single image super-resolution methods, to increase the spatial resolution of a given DEM dataset up to 4 times without additional information related to data. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Data analysis and data warehouse design based on Pentaho data integration (kettle) to support the determination of student learning achievement

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IOP Conference Series: Materials Science and engineering 2021年第5期1098卷

作者： A D Barahama R Wardani Electronics and Informatics Engineering Education Post-Graduate Program Yogyakarta State University Jalan Colombo 01 Daerah Istimewa Yogyakarta 55281 Indonesia

Data analysis has become a vital requirement in various fields. The results of data analysis can be used as a data warehouse design that is expected to help in solving problems, evaluating learning outcomes and supporting decision making. In the field of education, student achievement is a reference for achieving quality learning. This success can be seen from the achievement of student learning completeness that is the basis for evaluating and making decisions. The data warehouse design can be used as a basis in knowing student learning progress that can be seen from the value obtained. The diversity of data makes managing values difficult and delayed, data warehouse design using Pentaho can help and simplify data integration. The results of the analysis and design of the data warehouse will be presented in a multidimensional form that can be seen through dimension tables and fact tables.

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An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings

An End-to-End Approach for Recognition of Modern and Histori...

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International Joint Conference on Neural Networks (IJCNN)

作者： Andre G. Hochuli Alceu S. Britto Jean P. Barddal Robert Sabourin Luiz E. S. Oliveira Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná Curitiba PR Brazil System Engineering Dept. (LIVIA) École de Technologie Supérieure Montreal QC Canada Department of Informatics (DInf) Universidade Federal do Paraná Curitiba PR Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model. The main contribution of this paper is to avoid heuristic-based methods for string preprocessing and segmentation, the need for task-oriented classifiers, and also the use of specific constraints related to the string length. A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition. Besides, it reduces the complexity of the string recognition task considerably since it drops out classical steps, in special preprocessing, segmentation, and a set of classifiers devoted to strings with a specific length.

关键词： Handwriting recognition Training Feature extraction Computer architecture Proposals Machine learning Task analysis

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On the Performance of Industrial Wireless Sensor Networks: Channel Hopping vs. Channel Adaptive Protocols 1

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6th International Conference on Sensor Networks, SENSORNETS 2017 and 7th International Conference on Sensor Networks, SENSORNETS 2018

作者： Gomes, Ruan D. Benavente-Peces, Cesar Alencar, Marcelo S. Queiroz, Diego V. Fonseca, Iguatemi E. Post-Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Informatics Coordination Federal Institute of Paraiba Guarabira Brazil Signal Theory and Communications Department Universidad Politecnica de Madrid Madrid Spain Informatics Center Federal University of Paraiba Joao Pessoa Brazil

ISBN: (数字)9783030301101

ISBN: (纸本)9783030301095

The standard IEEE 802.15.4e shows several improvements which makes it outperforming the predecessor IEEE 802.15.4 standard. A remarkable improvement is the use of multiple channels and the capability to choose the most convenient depending on the network links state. Hence, different techniques can be provisioned to dynamically select the most appropriate channel for establishing the link through an adaptive mechanism. During the contention-free periods, the Time-Slotted Channel Hopping (TSCH) mode is based on channel hopping while the Deterministic and Synchronous Multi-channel Extension (DSME) mode uses either channel hopping or channel adaptation. In scenarios where channel diversity techniques are used for channel adaptation nodes links are established using the same channel as long as the channel quality (SNR) is high enough. In consequence to guarantee link best performance, its quality must be measured periodically to decide whether continue in the same channel of switch to a better performance one. Our investigation focuses on the analysis of the performance of three different approaches which pretend to take advantage of the multichannel capabilities of the new standard, based on the DSME protocol: CH-DSME which is based on a simple channel hopping mechanism, CA-DSME which employs channel adaptation, and a novel hybrid approach (H-DSME) which combines channel hopping and channel adaptation. This investigation shows that, in an industrial environment, the H-DSME outperforms both CH-DSME and CA-DSME approaches. In this work, the Castalia simulator was used to perform the simulations, using a reliable simulation model, which captures the characteristics of the wireless channel in industrial environments in long range basis. The analysis demonstrates that channel adaptation is better than channel hopping for unicast packets transmission, when links quality are continuously measured. Nevertheless, the coordinator transmit packets in broadcast mode, it is demonst

关键词： Wireless sensor networks

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Feasibility study of Information Technology Investment (a case study of ODOO ERP: Project Management Module Implementation in Indonesia Based Company) 10

Feasibility study of Information Technology Investment (a ca...

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Jingga, Fredy Kosala, Raymond Supangkat, Suhono Harso Ranti, Benny School of Information Systems Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728148809

IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to determine feasibility of implementation open source ERP system Odoo project management module that replaces current system based on operational, economic, and technical aspects. The method used are Fit/Gap Analysis and Cost Benefit model using economic impact worksheet. The analysis is done by comparing the requirement with features owned by current system and Odoo ERP system. This research resulted that Odoo ERP System is feasible in the 3 aspects, the operational feasibility Odoo reaching 72% Fit, the economic feasibility with ROI of 84%, and technical feasibility indicating the technical requirement can be fulfilled by leasing AWS server. The conclusion is the open source ERP system Odoo project management module is feasible to be applied in terms of 3 aspects that have been studied, those are operational, economic, and technical. © 2019 IEEE.

关键词： Cost benefit analysis

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TinyOS-Based WSN Performance: Default Active Message Layer vs. TKN15.4 1

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6th International Conference on Sensor Networks, SENSORNETS 2017 and 7th International Conference on Sensor Networks, SENSORNETS 2018

作者： Queiroz, Diego V. Benavente-Peces, Cesar Gomes, Ruan D. Signal Theory and Communications Department Universidad Politecnica de Madrid Madrid Spain Informatics Coordination Federal Institute of Paraiba Guarabira Brazil Post-Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil

ISBN: (数字)9783030301101

ISBN: (纸本)9783030301095

The applications aimed at monitoring and controlling smart homes and buildings, health, industrial environments, traffic, energy consumption, resources, and so on, are being increasingly deployed due to the advantages they provide, increasing efficiency and decreasing costs. In this context Wireless Sensor Networks (WSN) have become a leading solution providing the appropriate mean to get and collect data and deliver it for their processing. Sensor nodes in a WSN have resource constraints, presenting low processing power and, in some cases, restrictions in power consumption. Resources availability constraints force researchers and engineers to develop proper Operating Systems (OS) aimed at low-power wireless devices, and one of the most important and in active use is the TinyOS. This paper describes an experimental analysis and evaluation which investigates relevant features and evaluates the performance (throughput, network delay, success rate, and energy consumption) of WSN under the constraint of using the low-power-consumption-aimed chosen operating system in an harsh outdoor industrial environment for with two different protocols: Active Message (AM) layer protocol and the fully 802.15.4 compliant protocol stack TKN15.4. Based on the results we conclude that TKN15.4 is better in energy consumption and success rate. On the other hand, AM protocol allows running multiple services using the same radio, but at the cost of an excessive energy consumption. © 2019, Springer Nature Switzerland AG.

关键词： Electric power utilization

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