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Selected Papers From the 16th International Symposium on Advanced Intelligent Systems (ISIS)

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (21 April 2016) | Viewed by 39250

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Prof. Dr. Suk-Seung Hwang

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Department of Electronic Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759, Republic of Korea
Interests: adaptive signal processing; wireless communications; location detection technology; interference cancellation; channel estimation; GPS; RFID
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Prof. Dr. Euntai Kim

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Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Interests: pedestrian and vehicle detection; recognition; vision for advanced driver assistance systems (ADAS); robot vision
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Dr. Sungshin Kim

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Department of Electrical Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, Republic of Korea
Interests: intelligent system; fault diagnosis and prediction; solar energy; energy storage systems; deep learning
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Prof. Dr. Keon Myung Lee

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Department of Computer Science, Chungbuk National University, Cheongju, Korea
Interests: machine learning, data mining, soft computing, IoT data fusion, big data processing

Special Issue Information

Dear Colleagues,

This Special Issue will consist of selected excellent papers from the 16th ISIS (the International Symposium on Advanced Intelligent Systems), which will be held in Mokpo, Korea, from the 4th – 7th November, 2015. This international conference is designed to share a wide variety of ideas. Contributors will be invited to submit and present papers concerning the “convergence of intelligent systems and big data for smart life.” Topics of selected papers will include various sensor techniques, devices, and applications for intelligent systems. These papers are subjected to peer review and are published so as to widely disseminate new research results, including developments and applications.
The authors of papers submitted to the 16th ISIS (International Symposium on Advanced Intelligent Systems, http://www.isis2015.org) will be given the opportunity to submit extended versions of their works in this Special Issue, provided they fulfill the specific journal requirements found at https://www.mdpi.com/journal/sensors/instructions.

Topics of interest:
-      sensor network and communications
-      sensing for artificial intelligence, neural networks, neuro-fuzzy systems, chaotic systems, big data analysis, learning and adaptive systems.
-      sensing for intelligent control and robotics, intelligent manufacturing systems, mechatronics design.
-      vision and sensors.
-      fault detection and diagnosis, embedded real-time systems, and intelligent transportation systems based on sensors.
-      sensing techniques for intelligent home appliances, smart industrial systems, and medical imaging
-      location detection techniques based on sensing

Dr. Suk-seung Hwang
Prof. Dr. Euntai Kim
Prof. Dr. Sungshin Kim
Prof. Dr. Keon Myung Lee
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. Sensors 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 2600 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

sensors
network and communications
neural networks
neuro-fuzzy systems
artificial intelligence
location detection

Published Papers (6 papers)

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Research

7435 KiB

Open AccessArticle

Sine Rotation Vector Method for Attitude Estimation of an Underwater Robot

by Nak Yong Ko, Seokki Jeong and Youngchul Bae

Sensors 2016, 16(8), 1213; https://doi.org/10.3390/s16081213 - 02 Aug 2016

Cited by 15 | Viewed by 5470

Abstract

This paper describes a method for estimating the attitude of an underwater robot. The method employs a new concept of sine rotation vector and uses both an attitude heading and reference system (AHRS) and a Doppler velocity log (DVL) for the purpose of measurement. First, the acceleration and magnetic-field measurements are transformed into sine rotation vectors and combined. The combined sine rotation vector is then transformed into the differences between the Euler angles of the measured attitude and the predicted attitude; the differences are used to correct the predicted attitude. The method was evaluated according to field-test data and simulation data and compared to existing methods that calculate angular differences directly without a preceding sine rotation vector transformation. The comparison verifies that the proposed method improves the attitude estimation performance. Full article

(This article belongs to the Special Issue Selected Papers From the 16th International Symposium on Advanced Intelligent Systems (ISIS))

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4349 KiB

Open AccessArticle

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner

by Jhonghyun An, Baehoon Choi, Kwee-Bo Sim and Euntai Kim

Sensors 2016, 16(7), 1123; https://doi.org/10.3390/s16071123 - 20 Jul 2016

Cited by 9 | Viewed by 5188

Abstract

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation. Full article

(This article belongs to the Special Issue Selected Papers From the 16th International Symposium on Advanced Intelligent Systems (ISIS))

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5588 KiB

Open AccessArticle

Development of Torque Sensor with High Sensitivity for Joint of Robot Manipulator Using 4-Bar Linkage Shape

by Hong-Xia Zhang, Young-Jae Ryoo and Kyung-Seok Byun

Sensors 2016, 16(7), 991; https://doi.org/10.3390/s16070991 - 01 Jul 2016

Cited by 19 | Viewed by 9174

Abstract

The torque sensor is used to measure the joint torque of a robot manipulator. Previous research showed that the sensitivity and the stiffness of torque sensors have trade-off characteristics. Stiffness has to be sacrificed to increase the sensitivity of the sensor. In this research, a new torque sensor with high sensitivity (TSHS) is proposed in order to resolve this problem. The key idea of the TSHS comes from its 4-bar linkage shape in which the angular displacement of a short link is larger than that of a long link. The sensitivity of the torque sensor with a 4-bar link shape is improved without decreasing stiffness. Optimization techniques are applied to maximize the sensitivity of the sensor. An actual TSHS is constructed to verify the validity of the proposed mechanism. Experimental results show that the sensitivity of TSHS can be increased 3.5 times without sacrificing stiffness. Full article

(This article belongs to the Special Issue Selected Papers From the 16th International Symposium on Advanced Intelligent Systems (ISIS))

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1032 KiB

Open AccessArticle

Robust Control for the Segway with Unknown Control Coefficient and Model Uncertainties

by Byung Woo Kim and Bong Seok Park

Sensors 2016, 16(7), 1000; https://doi.org/10.3390/s16071000 - 29 Jun 2016

Cited by 22 | Viewed by 7715

Abstract

The Segway, which is a popular vehicle nowadays, is an uncertain nonlinear system and has an unknown time-varying control coefficient. Thus, we should consider the unknown time-varying control coefficient and model uncertainties to design the controller. Motivated by this observation, we propose a robust control for the Segway with unknown control coefficient and model uncertainties. To deal with the time-varying unknown control coefficient, we employ the Nussbaum gain technique. We introduce an auxiliary variable to solve the underactuated problem. Due to the prescribed performance control technique, the proposed controller does not require the adaptive technique, neural network, and fuzzy logic to compensate the uncertainties. Therefore, it can be simple. From the Lyapunov stability theory, we prove that all signals in the closed-loop system are bounded. Finally, we provide the simulation results to demonstrate the effectiveness of the proposed control scheme. Full article

(This article belongs to the Special Issue Selected Papers From the 16th International Symposium on Advanced Intelligent Systems (ISIS))

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619 KiB

Open AccessArticle

Investigating the Impact of Possession-Way of a Smartphone on Action Recognition

by Zae Myung Kim, Young-Seob Jeong, Hyung Rai Oh, Kyo-Joong Oh, Chae-Gyun Lim, Youssef Iraqi and Ho-Jin Choi

Sensors 2016, 16(6), 812; https://doi.org/10.3390/s16060812 - 02 Jun 2016

Cited by 3 | Viewed by 4810

Abstract

For the past few decades, action recognition has been attracting many researchers due to its wide use in a variety of applications. Especially with the increasing number of smartphone users, many studies have been conducted using sensors within a smartphone. However, a lot of these studies assume that the users carry the device in specific ways such as by hand, in a pocket, in a bag, etc. This paper investigates the impact of providing an action recognition system with the information of the possession-way of a smartphone, and vice versa. The experimental dataset consists of five possession-ways (hand, backpack, upper-pocket, lower-pocket, and shoulder-bag) and two actions (walking and running) gathered by seven users separately. Various machine learning models including recurrent neural network architectures are employed to explore the relationship between the action recognition and the possession-way recognition. The experimental results show that the assumption of possession-ways of smartphones do affect the performance of action recognition, and vice versa. The results also reveal that a good performance is achieved when both actions and possession-ways are recognized simultaneously. Full article

(This article belongs to the Special Issue Selected Papers From the 16th International Symposium on Advanced Intelligent Systems (ISIS))

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2357 KiB

Open AccessArticle

An Improved Measurement Method for the Strength of Radiation of Reflective Beam in an Industrial Optical Sensor Based on Laser Displacement Meter

by Youngchul Bae

Sensors 2016, 16(5), 752; https://doi.org/10.3390/s16050752 - 23 May 2016

Cited by 10 | Viewed by 5978

Abstract

An optical sensor such as a laser range finder (LRF) or laser displacement meter (LDM) uses reflected and returned laser beam from a target. The optical sensor has been mainly used to measure the distance between a launch position and the target. However, optical sensor based LRF and LDM have numerous and various errors such as statistical errors, drift errors, cyclic errors, alignment errors and slope errors. Among these errors, an alignment error that contains measurement error for the strength of radiation of returned laser beam from the target is the most serious error in industrial optical sensors. It is caused by the dependence of the measurement offset upon the strength of radiation of returned beam incident upon the focusing lens from the target. In this paper, in order to solve these problems, we propose a novel method for the measurement of the output of direct current (DC) voltage that is proportional to the strength of radiation of returned laser beam in the received avalanche photo diode (APD) circuit. We implemented a measuring circuit that is able to provide an exact measurement of reflected laser beam. By using the proposed method, we can measure the intensity or strength of radiation of laser beam in real time and with a high degree of precision. Full article

(This article belongs to the Special Issue Selected Papers From the 16th International Symposium on Advanced Intelligent Systems (ISIS))

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