Journals / IASC / Vol.,

Research Article

BEST PAPER 2021

Machine Learning and Deep Learning for Transportation


Submission Deadline: 30 March 2021 (closed)

Abstract

This article has no abstract.

Keywords

• Machine learning
• Deep learning
• Convolutional neural network
• Recurrent neural network
• Intelligent Transportation
  • Research Article

    BEST PAPER 2021

    Predicting the Breed of Dogs and Cats with Fine-Tuned Keras Applications

    I.-Hung Wang1, Mahardi2, Kuang-Chyi Lee2,*, Shinn-Liang Chang1 Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 995-1005, 2021, DOI:10.32604/iasc.2021.019020
    Abstract The images classification is one of the most common applications of deep learning. Images of dogs and cats are mostly used as examples for image classification models, as they are relatively easy for the human eyes to recognize. However, classifying the breed of a dog or a cat has its own complexity. In this paper, a fine-tuned pre-trained model of a Keras’ application was built with a new dataset of dogs and cats to predict the breed of identified dogs or cats. Keras applications are deep learning models, which have been previously trained with general image datasets from ImageNet. In… More >

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  • Research Article

    BEST PAPER 2021

    A Novel Automatic Meal Delivery System

    Jhe-Wei Lin1, Cheng-Yan Siao1, Ting-Hsuan Chien2,*, Rong-Guey Chang1 Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 685-695, 2021, DOI:10.32604/iasc.2021.018254
    Abstract Since the rapid growth of the Fourth Industrial Revolution (or Industry 4.0), robots have been widely used in many applications. In the catering industry, robots are used to replace people to do routine jobs. Because meal is an important part of the catering industry, we aim to design and develop a robot to deliver meals for saving cost and improving a restaurant’s performance in this paper. However, for the existing meal delivery system, the guests must make their meals by themselves. To let the food delivery system become more user-friendly, we integrate an automatic guided vehicle (AGV) and a robotic… More >

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  • Research Article

    BEST PAPER 2021

    A General Technique for Real-Time Robotic Simulation in Manufacturing System

    Ting-Hsuan Chien1,*, Cheng-Yan Siao2, Rong-Guey Chang2 Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 827-838, 2021, DOI:10.32604/iasc.2021.018256
    Abstract This paper describes a real-time simulator that allows the user in the factories to simulate arbitrary interaction between machinery and equipment. We discussed in details not only the general technique for developing such a real-time simulator but also the implementation of the simulator in its actual use. As such, people on the production line could benefit from observing and controlling robots in factories for preventing or reducing the severity of a collision, using the proposed simulator and its related technique. For that purpose, we divided the simulator into two main models: the real-time communication model and the simulation model. For… More >

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  • Research Article

    BEST PAPER 2021

    Paralleling Collision Detection on Five-Axis Machining

    Cheng-Yan Siao1, Jhe-Wei Lin1, Ting-Hsuan Chien2,*, Rong-Guey Chang1 Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 559-569, 2021, DOI:10.32604/iasc.2021.018252
    Abstract With the rapid growth of the Fourth Industrial Revolution (or Industry 4.0), five-axis machining has played an important role nowadays. Due to the expensive cost of five-axis machining, how to solve the collision detection for five-axis machining in real-time is very critical. In this paper, we present a parallel method to detect collision for five-axis machining. Moreover, we apply the bounding volume hierarchy technique with two-level bounding volume represent the surface or solid of the object to reduce triangle meshes inside each axis of the five-axis machine tool, and then matching the operating range limit of the five-axis machine tool… More >

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  • Research Article

    BEST PAPER 2021

    A Deep Learning Approach for the Mobile-Robot Motion Control System

    Rihem Farkh1,4,*, Khaled Al jaloud1, Saad Alhuwaimel2, Mohammad Tabrez Quasim3, Moufida Ksouri4 Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 423-435, 2021, DOI:10.32604/iasc.2021.016219
    Abstract A line follower robot is an autonomous intelligent system that can detect and follow a line drawn on floor. Line follower robots need to adapt accurately, quickly, efficiently, and inexpensively to changing operating conditions. This study proposes a deep learning controller for line follower mobile robots using complex decision-making strategies. An Arduino embedded platform is used to implement the controller. A multilayered feedforward network with a backpropagation training algorithm is employed. The network is trained offline using Keras and implemented on a ATmega32 microcontroller. The experimental results show that it has a good control effect and can extend its application. More >

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  • Research Article

    BEST PAPER 2021

    Implementation of Multi-Object Recognition System for the Blind

    Huijin Park, Soobin Ou, Jongwoo Lee* Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 247-258, 2021, DOI:10.32604/iasc.2021.015274
    Abstract Blind people are highly exposed to numerous dangers when they walk alone outside as they cannot obtain sufficient information about their surroundings. While proceeding along a crosswalk, acoustic signals are played, though such signals are often faulty or difficult to hear. The bollards can also be dangerous if they are not made with flexible materials or are located improperly. Therefore, since the blind cannot detect proper information about these obstacles while walking, their environment can prove to be dangerous. In this paper, we propose an object recognition system that allows the blind to walk safely outdoors. The proposed system can… More >

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  • Research Article

    BEST PAPER 2021

    Analysis of Roadside Accident Severity on Rural and Urban Roadways

    Fulu Wei1,2, Zhenggan Cai1, Yongqing Guo1,*, Pan Liu2, Zhenyu Wang3, Zhibin Li2 Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 753-767, 2021, DOI:10.32604/iasc.2021.014661
    Abstract The differences in traffic accident severity between urban and rural areas have been widely studied, but conclusions are still limited. To explore the factors influencing the occurrence of roadside accidents in urban and rural areas, 3735 roadside traffic accidents from 2017 to 2019 were analyzed. Fourteen variables from the aspects of driver, vehicle, driving environment, and other influencing factors were selected to establish a Bayesian binary logit model of roadside crashes. The deviance information criterion and receiver operating characteristic curve were used to test the goodness of fit for the traffic crash model. The results show that: (1) the Bayesian… More >

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  • Research Article

    BEST PAPER 2021

    Driving Pattern Profiling and Classification Using Deep Learning

    Meenakshi Malik1, Rainu Nandal1, Surjeet Dalal2, Vivek Jalglan3, Dac-Nhuong Le4,5,* Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 887-906, 2021, DOI:10.32604/iasc.2021.016272
    Abstract The last several decades have witnessed an exponential growth in the means of transport globally, shrinking geographical distances and connecting the world. The automotive industry has grown by leaps and bounds, with millions of new vehicles being sold annually, be it for personal commuting or for public or commodity transport. However, millions of motor vehicles on the roads also mean an equal number of drivers with varying levels of skill and adherence to safety regulations. Very little has been done in the way of exploring and profiling driving patterns and vehicular usage using real world data. This paper focuses on… More >

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  • Research Article

    BEST PAPER 2021

    Constructional Cyber Physical System: An Integrated Model

    Tzer-Long Chen1, Chien-Yun Chang2, Yung-Cheng Yao3, Kuo-Chang Chung4,* Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 73-82, 2021, DOI:10.32604/iasc.2021.015980
    Abstract Artificial intelligence, machine learning, and deep learning have achieved great success in the fields of computer vision and natural language processing, and then extended to various fields, such as biology, chemistry, and civil engineering, including big data in the field of logistics. Therefore, many logistics companies move towards the integration of intelligent transportation systems. Only virtual and physical development can support the sustainable development of the logistics industry. This study aims to: 1.) collect timely information from the block chain, 2.) use deep learning to build a customer database so that sales staff in physical stores can grasp customer preferences,… More >

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  • Research Article

    BEST PAPER 2021

    Design and Validation of a Route Planner for Logistic UAV Swarm

    Meng-Tse Lee1,*, Ying-Chih Lai2, Ming-Lung Chuang1, Bo-Yu Chen1 Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 227-240, 2021, DOI:10.32604/iasc.2021.015339
    Abstract Unmanned Aerial Vehicles (UAV) are widely used in different fields of aviation today. The efficient delivery of packages by drone may be one of the most promising applications of this technology. In logistic UAV missions, due to the limited capacities of power supplies, such as fuel or batteries, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Thus, multiple unmanned vehicles with well-planned routes become necessary to minimize the unnecessary consumption of time, distance, and energy while carrying out the delivery missions. The aim of the present study was to develop a multiple-vehicle mission dispatch system… More >

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  • Research Article

    BEST PAPER 2021

    Parallel Equilibrium Optimizer Algorithm and Its Application in Capacitated Vehicle Routing Problem

    Zonglin Fu1, Pei Hu1, Wei Li2, Jeng-Shyang Pan1,*, Shuchuan Chu1 Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 233-247, 2021, DOI:10.32604/iasc.2021.014192
    Abstract The Equilibrium Optimizer (EO) algorithm is a novel meta-heuristic algorithm based on the strength of physics. To achieve better global search capability, a Parallel Equilibrium Optimizer algorithm, named PEO, is proposed in this paper. PEO is inspired by the idea of parallelism and adopts two different communication strategies between groups to improve EO. The first strategy is used to speed up the convergence rate and the second strategy promotes the algorithm to search for a better solution. These two kinds of communication strategies are used in the early and later iterations of PEO respectively. To check the optimization effect of… More >

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