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Selected Papers from IEEE ICKII 2018
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Selected Papers from IEEE ICKII 2018

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 23161

Special Issue Editors

Prof. Dr. Teen­-Hang Meen

E-Mail Website
Guest Editor
Department of Electronic Engineering National Formosa University, Yunlin 632, Taiwan
Interests: IOT devices; photovoltaic devices; STEM education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenbing Zhao

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: human-centered systems; machine learning; data science; distributed computing; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

1st IEEE International Conference on Knowledge Innovation and Invention 2018 (IEEE ICKII 2018) will be held in Jeju Island, South Korea on July 23-27, 2018, and it will provide a unified communication platform for researchers in the topics of information technology, innovation design, communication science & engineering, industrial design, creative design, applied mathematics, computer science, electrical & electronic engineering, mechanical & automation engineering, green technology & architecture engineering, material science and other related fields. The special issue on “Selected papers from IEEE ICKII 2018” is expected to select excellent papers presented in IEEE ICKII 2018 about the topic of “Innovation of Applied System”. Mechanical Engineering and Design Innovations are both an academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation by Mechanical Engineering includes IT-based Intelligent Mechanical Systems, Mechanics and Design Innovations, and Applied Materials on Nanosciences and Nanotechnology. These new technologies which implant intelligence to machine systems, are the interdisciplinary area combining conventional mechanical technology and new information technology.

The main goal of this special issue “Selected papers from IEEE ICKII 2018” is to discover new scientific knowledge relevant to IT-based Intelligent Mechanical Systems, Mechanics and Design Innovations, and Applied Materials on Nanosciences and Nanotechnology. We invite investigators interested in Applied System Innovation to contribute their original research articles to this Special Issue. Potential topics include, but are not limited to:

  • Intelligent mechanical manufacturing system
  • Mathematical problems on mechanical system design.
  • Smart electromechanical system analysis and design
  • Applied Materials on Nanosciences and Nanotechnology
  • Computer-aided methods for mechanical design procedure and manufacture.
  • Computer and human-machine interaction.
  • Internet Technology on mechanical system innovation.
  • Machine diagnostics & reliability
  • Human-machine interaction/Virtual reality and entertainment

Prof. Dr. Teen­-Hang Meen
Prof. Dr. Wenbing Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart electromechanical system analysis and design
  • Intelligent mechanical System
  • Applied Materials on Nanosciences and Nanotechnology

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Published Papers (4 papers)

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Research

25 pages, 4413 KiB  
Article
Classification of Liver Diseases Based on Ultrasound Image Texture Features
by Sendren Sheng-Dong Xu, Chun-Chao Chang, Chien-Tien Su and Pham Quoc Phu
Appl. Sci. 2019, 9(2), 342; https://doi.org/10.3390/app9020342 - 19 Jan 2019
Cited by 38 | Viewed by 8005
Abstract
This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and [...] Read more.
This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier. Among 79 cases of liver diseases including 44 cases of liver cancer and 35 cases of liver abscess, this research extracts 96 features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) from the regions of interest (ROIs) in ultrasound images. Three feature selection models—(i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score—are adopted to distinguish the two liver diseases. Finally, the developed system can classify liver cancer and liver abscess by SVM with an accuracy of 88.875%. The proposed methods for CAD can provide diagnostic assistance while distinguishing these two types of liver lesions. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2018)
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10 pages, 2522 KiB  
Article
Long Term Stable Δ-Σ NDIR Technique Based on Temperature Compensation
by Chih-Hsiung Shen and Jun-Hong Yeah
Appl. Sci. 2019, 9(2), 309; https://doi.org/10.3390/app9020309 - 16 Jan 2019
Cited by 6 | Viewed by 5259
Abstract
For a fast and long term stable Non Dispersive Infrared (NDIR) technology of gas concentration measurement, the temperature compensation is required. A novel proposed Δ-Σ NDIR system was investigated and built with a closed-loop feedback system to stabilize the signal readings without temperature [...] Read more.
For a fast and long term stable Non Dispersive Infrared (NDIR) technology of gas concentration measurement, the temperature compensation is required. A novel proposed Δ-Σ NDIR system was investigated and built with a closed-loop feedback system to stabilize the signal readings without temperature drift. The modulation of the infrared heater gives a corresponding signal of gas concentration based on our proposed Δ-Σ conversion algorithm that was affected by the drift of temperatures for the infrared sensor. For our study, a new temperature compensation model was built and verified that formulates the relationship between gas concentration and temperature of sensor. The results show that our proposed Δ-Σ can measure efficiently with half of the startup time than our previous design and maintain long term stability. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2018)
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16 pages, 9719 KiB  
Article
An Online Simultaneous Measurement of the Dual-Axis Straightness Error for Machine Tools
by Wen-Yuh Jywe, Tung-Hsien Hsieh, Po-Yu Chen and Ming-Shi Wang
Appl. Sci. 2018, 8(11), 2130; https://doi.org/10.3390/app8112130 - 2 Nov 2018
Cited by 7 | Viewed by 3978
Abstract
Vertical straightness errors are the key factor that affects the flatness of the workpiece during vertical machining. Traditionally, the individually measured and fitted vertical straightness errors of the X and Y axes are used to compensate the Z axis and, thus, obtain the [...] Read more.
Vertical straightness errors are the key factor that affects the flatness of the workpiece during vertical machining. Traditionally, the individually measured and fitted vertical straightness errors of the X and Y axes are used to compensate the Z axis and, thus, obtain the flatness of the working table of the machine tool. However, it is difficult to measure and compensate the vertical straightness error of the desired position on the working table, not to mention the centroid variation effect of the working table on the measured data. In this study, an online dual-axis measurement system with repeatability (3σ) of 2.46 μm is developed to simultaneously measure X-axis and Y-axis straightness errors of the desired position of a working table. Furthermore, the measured data are utilized to establish a flatness error model to reduce the vertical straightness error of the working table such that the repeatability (3σ) of the measured flatness may be kept within a range of 0.65 μm. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2018)
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26 pages, 13873 KiB  
Article
Integrated High-Performance Platform for Fast Query Response in Big Data with Hive, Impala, and SparkSQL: A Performance Evaluation
by Bao Rong Chang, Hsiu-Fen Tsai and Yun-Da Lee
Appl. Sci. 2018, 8(9), 1514; https://doi.org/10.3390/app8091514 - 1 Sep 2018
Cited by 4 | Viewed by 4824
Abstract
This paper first integrates big data tools—Hive, Impala, and SparkSQL—which support SQL-like queries for rapid data retrieval in big data. The three introduced tools are not only suitable for operating in business intelligence to serve high-performance data retrieval, but they are also an [...] Read more.
This paper first integrates big data tools—Hive, Impala, and SparkSQL—which support SQL-like queries for rapid data retrieval in big data. The three introduced tools are not only suitable for operating in business intelligence to serve high-performance data retrieval, but they are also an open-source software solution with low cost for small-to-medium enterprise use. In practice, the proposed approach provides an in-memory cache and an in-disk cache to achieve a very fast response to a query if a cache hit occurs. Moreover, this paper develops so-called platform selection that is able to select the appropriate tool dealing with input query with effectiveness and efficiency. As a result, the speed of job execution of proposed approach using platform selection is 2.63 times faster than Hive in the Case 1 experiment, and 4.57 times faster in the Case 2 experiment. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2018)
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