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21 pages, 1671 KiB

Open AccessArticle

LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection

by Muhammad Adil, Nadeem Javaid, Umar Qasim, Ibrar Ullah, Muhammad Shafiq and Jin-Ghoo Choi

Appl. Sci. 2020, 10(12), 4378; https://doi.org/10.3390/app10124378 - 25 Jun 2020

Cited by 80 | Viewed by 5447

Abstract

The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system [...] Read more.

The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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10 pages, 7777 KiB

Open AccessArticle

Simpler Learning of Robotic Manipulation of Clothing by Utilizing DIY Smart Textile Technology

by Andreas Verleysen, Thomas Holvoet, Remko Proesmans, Cedric Den Haese and Francis wyffels

Appl. Sci. 2020, 10(12), 4088; https://doi.org/10.3390/app10124088 - 13 Jun 2020

Cited by 6 | Viewed by 3216

Abstract

Deformable objects such as ropes, wires, and clothing are omnipresent in society and industry but are little researched in robotics research. This is due to the infinite amount of possible state configurations caused by the deformations of the deformable object. Engineered approaches try [...] Read more.

Deformable objects such as ropes, wires, and clothing are omnipresent in society and industry but are little researched in robotics research. This is due to the infinite amount of possible state configurations caused by the deformations of the deformable object. Engineered approaches try to cope with this by implementing highly complex operations in order to estimate the state of the deformable object. This complexity can be circumvented by utilizing learning-based approaches, such as reinforcement learning, which can deal with the intrinsic high-dimensional state space of deformable objects. However, the reward function in reinforcement learning needs to measure the state configuration of the highly deformable object. Vision-based reward functions are difficult to implement, given the high dimensionality of the state and complex dynamic behavior. In this work, we propose the consideration of concepts beyond vision and incorporate other modalities which can be extracted from deformable objects. By integrating tactile sensor cells into a textile piece, proprioceptive capabilities are gained that are valuable as they provide a reward function to a reinforcement learning agent. We demonstrate on a low-cost dual robotic arm setup that a physical agent can learn on a single CPU core to fold a rectangular patch of textile in the real world based on a learned reward function from tactile information. Full article

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22 pages, 1271 KiB

Open AccessArticle

Blockchain-Based Secure Data Storage for Distributed Vehicular Networks

by Muhammad Umar Javed, Mubariz Rehman, Nadeem Javaid, Abdulaziz Aldegheishem, Nabil Alrajeh and Muhammad Tahir

Appl. Sci. 2020, 10(6), 2011; https://doi.org/10.3390/app10062011 - 16 Mar 2020

Cited by 77 | Viewed by 6781

Abstract

In this paper, a blockchain-based secure data sharing mechanism is proposed for Vehicular Networks (VNs). Edge service providers are introduced along with ordinary nodes to efficiently manage service provisioning. The edge service providers are placed in the neighborhood of the ordinary nodes to [...] Read more.

In this paper, a blockchain-based secure data sharing mechanism is proposed for Vehicular Networks (VNs). Edge service providers are introduced along with ordinary nodes to efficiently manage service provisioning. The edge service providers are placed in the neighborhood of the ordinary nodes to ensure smooth communication between them. The huge amount of data generated by smart vehicles is stored in a distributed file storage system, known as Interplanetary File System (IPFS). It is used to tackle the issues related to data storage in centralized architectures, such as data tampering, lack of privacy, vulnerability to hackers, etc. Monetary incentives are given to edge vehicle nodes to motivate them for accurate and timely service provisioning to ordinary nodes. In response, ordinary nodes give reviews to the edge nodes against the services provided by them, which are further stored in a blockchain to ensure integrity, security and transparency. Smart contracts are used to automate the system processes without the inclusion of an intermediate party and to check the reviews given to the edge nodes. To optimize gas consumption and to enhance the system performance, a Proof of Authority (PoA) consensus mechanism is used to validate the transactions. Moreover, a caching system is introduced at the edge nodes to store frequently used services. Furthermore, both security and privacy are enhanced in the proposed system by incorporating a symmetric key cryptographic mechanism. A trust management mechanism is also proposed in this work to calculate the nodes’ reputation values based upon their trust values. These values determine the authenticity of the nodes involved in the network. Eventually, it is concluded from the simulation results that the proposed system is efficient for VNs. Full article

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21 pages, 1731 KiB

Open AccessArticle

Data Sharing System Integrating Access Control Mechanism using Blockchain-Based Smart Contracts for IoT Devices

by Tanzeela Sultana, Ahmad Almogren, Mariam Akbar, Mansour Zuair, Ibrar Ullah and Nadeem Javaid

Appl. Sci. 2020, 10(2), 488; https://doi.org/10.3390/app10020488 - 9 Jan 2020

Cited by 134 | Viewed by 12143

Abstract

In this paper, a blockchain-based data sharing and access control system is proposed, for communication between the Internet of Things (IoT) devices. The proposed system is intended to overcome the issues related to trust and authentication for access control in IoT networks. Moreover, [...] Read more.

In this paper, a blockchain-based data sharing and access control system is proposed, for communication between the Internet of Things (IoT) devices. The proposed system is intended to overcome the issues related to trust and authentication for access control in IoT networks. Moreover, the objectives of the system are to achieve trustfulness, authorization, and authentication for data sharing in IoT networks. Multiple smart contracts such as Access Control Contract (ACC), Register Contract (RC), and Judge Contract (JC) are used to provide efficient access control management. Where ACC manages overall access control of the system, and RC is used to authenticate users in the system, JC implements the behavior judging method for detecting misbehavior of a subject (i.e., user). After the misbehavior detection, a penalty is defined for that subject. Several permission levels are set for IoT devices’ users to share services with others. In the end, performance of the proposed system is analyzed by calculating cost consumption rate of smart contracts and their functions. A comparison is made between existing and proposed systems. Results show that the proposed system is efficient in terms of cost. The overall execution cost of the system is 6,900,000 gas units and the transaction cost is 5,200,000 gas units. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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13 pages, 5453 KiB

Open AccessArticle

Parallel Insertion and Indexing Method for Large Amount of Spatiotemporal Data Using Dynamic Multilevel Grid Technique

by Sangdeok Park, Daesik Ko and Seokil Song

Appl. Sci. 2019, 9(20), 4261; https://doi.org/10.3390/app9204261 - 11 Oct 2019

Cited by 6 | Viewed by 2460

Abstract

In this paper, we propose a method to ingest big spatiotemporal data using a parallel technique in a cluster environment. The proposed method includes an indexing method for effective retrieval in addition to the parallel ingestion method of spatiotemporal data. In this paper, [...] Read more.

In this paper, we propose a method to ingest big spatiotemporal data using a parallel technique in a cluster environment. The proposed method includes an indexing method for effective retrieval in addition to the parallel ingestion method of spatiotemporal data. In this paper, a dynamic multilevel grid index scheme is proposed to maximize parallelism and to adapt to the skewed spatiotemporal data. Finally, through experiments in a cluster environment, it is shown that the ingestion and query throughput increase as the number of nodes increases. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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26 pages, 22961 KiB

Open AccessArticle

An Intelligent and Smart Environment Monitoring System for Healthcare

by Hina Sattar, Imran Sarwar Bajwa, Riaz Ul-Amin, Aqsa Mahmood, Waheed Anwar, Bakhtiar Kasi, Rafaqut Kazmi and Umar Farooq

Appl. Sci. 2019, 9(19), 4172; https://doi.org/10.3390/app9194172 - 5 Oct 2019

Cited by 7 | Viewed by 5951

Abstract

Skin wound healing is influenced by two kinds of environment i.e., exterior environment that is nearby to wound surface and interior environment that is the environment of the adjacent part under wound surface. Both types of environment play a vital role in wound [...] Read more.

Skin wound healing is influenced by two kinds of environment i.e., exterior environment that is nearby to wound surface and interior environment that is the environment of the adjacent part under wound surface. Both types of environment play a vital role in wound healing, which may contribute to continuous or impaired wound healing. Although, different previous studies provided wound care solutions, but they focused on single environmental factors either wound moisture level, pH value or healing enzymes. Practically, it is insignificant to consider environmental effect by determination of single factors or two, as both types of environment contain a lot of other factors which must be part of investigation e.g., smoke, air pollution, air humidity, temperature, hydrogen gases etc. Also, previous studies didn’t classify overall healing either as continuous or impaired based on exterior environment effect. In current research work, we proposed an effective wound care solution based on exterior environment monitoring system integrated with Neural Network Model to consider exterior environment effect on wound healing process, either as continuous or impaired. Current research facilitates patients by providing them intelligent wound care solution to monitor and control wound healing at their home. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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19 pages, 4576 KiB

Open AccessArticle

A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot

by Tao Yang, Xueshan Gao, Rui Gao, Fuquan Dai and Jinmin Peng

Appl. Sci. 2019, 9(19), 3986; https://doi.org/10.3390/app9193986 - 23 Sep 2019

Cited by 16 | Viewed by 2560

Abstract

Robot-aided training strategies that allow functional, assist-as-needed, or challenging training have been widely explored. Accurate activity recognition is the basis for implementing alternative training strategies. However, some obstacles to accurate recognition exist. First, scientists do not yet fully understand some rehabilitation activities, such [...] Read more.

Robot-aided training strategies that allow functional, assist-as-needed, or challenging training have been widely explored. Accurate activity recognition is the basis for implementing alternative training strategies. However, some obstacles to accurate recognition exist. First, scientists do not yet fully understand some rehabilitation activities, such as abnormal gaits and falls; thus, there is no standardized feature for identifying such activities. Second, during the activity identification process, it is difficult to reasonably balance sensitivity and specificity when setting the threshold. Therefore, we proposed a multisensor fusion system and a two-stage activity recognition classifier. This multisensor system integrates explicit information such as kinematics and spatial distribution information along with implicit information such as kinetics and pulse information. Both the explicit and implicit information are analyzed in one discriminant function to obtain a detailed and accurate recognition result. Then, alternative training strategies can be implemented on this basis. Finally, we conducted experiments to verify the feasibility and efficiency of the multisensor fusion system. The experimental results show that the proposed fusion system achieves an accuracy of 99.37%, and the time required to prejudge a fall is approximately 205 milliseconds faster than the response time of single-sensor systems. Moreover, the proposed system also identifies fall directions and abnormal gait types. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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20 pages, 6338 KiB

Open AccessArticle

Intelligent Tennis Robot Based on a Deep Neural Network

by Shenshen Gu, Wei Zeng, Yuxuan Jia and Zheng Yan

Appl. Sci. 2019, 9(18), 3746; https://doi.org/10.3390/app9183746 - 8 Sep 2019

Cited by 7 | Viewed by 3656

Abstract

In this paper, an improved you only look once (YOLOv3) algorithm is proposed to make the detection effect better and improve the performance of a tennis ball detection robot. The depth-separable convolution network is combined with the original YOLOv3 and the residual block [...] Read more.

In this paper, an improved you only look once (YOLOv3) algorithm is proposed to make the detection effect better and improve the performance of a tennis ball detection robot. The depth-separable convolution network is combined with the original YOLOv3 and the residual block is added to extract the features of the object. The feature map output by the residual block is merged with the target detection layer through the shortcut layer to improve the network structure of YOLOv3. Both the original model and the improved model are trained by the same tennis ball data set. The results show that the recall is improved from 67.70% to 75.41% and the precision is 88.33%, which outperforms the original 77.18%. The recognition speed of the model is increased by half and the weight is reduced by half after training. All these features provide a great convenience for the application of the deep neural network in embedded devices. Our goal is that the robot is capable of picking up more tennis balls as soon as possible. Inspired by the maximum clique problem (MCP), the pointer network (Ptr-Net) and backtracking algorithm (BA) are utilized to make the robot find the place with the highest concentration of tennis balls. According to the training results, when the number of tennis balls is less than 45, the accuracy of determining the concentration of tennis balls can be as high as 80%. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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12 pages, 1155 KiB

Open AccessArticle

Feedback Stabilization of First Order Neutral Delay Systems Using the Lambert W Function

by Beomsoo Kim, Jaesung Kwon, Sungwoong Choi and Jeonghyeon Yang

Appl. Sci. 2019, 9(17), 3539; https://doi.org/10.3390/app9173539 - 28 Aug 2019

Cited by 6 | Viewed by 2164

Abstract

This paper presents a new approach to stabilize the first order neutral delay differential systems with two time delays. First, we provided a few oscillation and non-oscillation criteria for the neutral delay differential equations using spectrum analysis and the Lambert W function. These [...] Read more.

This paper presents a new approach to stabilize the first order neutral delay differential systems with two time delays. First, we provided a few oscillation and non-oscillation criteria for the neutral delay differential equations using spectrum analysis and the Lambert W function. These conditions were explicit and the real roots were analytically expressed in terms of the Lambert W function in the case of non-oscillation. Second, we designed a stabilizing state feedback controller for the neutral delay differential systems with two time delays, wherein the proportional and derivative gains were analytically determined using the results of the non-oscillation criteria. A few examples are given to illustrate the main results. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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17 pages, 7393 KiB

Open AccessArticle

High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

by Ye Chen, Guoliang Tao and Hao Liu

Appl. Sci. 2019, 9(17), 3472; https://doi.org/10.3390/app9173472 - 22 Aug 2019

Cited by 9 | Viewed by 3446

Abstract

In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network [...] Read more.

In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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11 pages, 2350 KiB

Open AccessArticle

Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network

by Min Seop Lee, Yun Kyu Lee, Dong Sung Pae, Myo Taeg Lim, Dong Won Kim and Tae Koo Kang

Appl. Sci. 2019, 9(16), 3355; https://doi.org/10.3390/app9163355 - 15 Aug 2019

Cited by 69 | Viewed by 8809

Abstract

Physiological signals contain considerable information regarding emotions. This paper investigated the ability of photoplethysmogram (PPG) signals to recognize emotion, adopting a two-dimensional emotion model based on valence and arousal to represent human feelings. The main purpose was to recognize short term emotion using [...] Read more.

Physiological signals contain considerable information regarding emotions. This paper investigated the ability of photoplethysmogram (PPG) signals to recognize emotion, adopting a two-dimensional emotion model based on valence and arousal to represent human feelings. The main purpose was to recognize short term emotion using a single PPG signal pulse. We used a one-dimensional convolutional neural network (1D CNN) to extract PPG signal features to classify the valence and arousal. We split the PPG signal into a single 1.1 s pulse and normalized it for input to the neural network based on the personal maximum and minimum values. We chose the dataset for emotion analysis using physiological (DEAP) signals for the experiment and tested the 1D CNN as a binary classification (high or low valence and arousal), achieving the short-term emotion recognition of 1.1 s with 75.3% and 76.2% valence and arousal accuracies, respectively, on the DEAP data. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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20 pages, 10100 KiB

Open AccessArticle

A Novel Virtual Sensor for Estimating Robot Joint Total Friction Based on Total Momentum

by Tian Xu, Jizhuang Fan, Qianqian Fang, Shoulong Wang, Yanhe Zhu and Jie Zhao

Appl. Sci. 2019, 9(16), 3344; https://doi.org/10.3390/app9163344 - 14 Aug 2019

Cited by 7 | Viewed by 2999

Abstract

Robot joint friction is an important and complicated issue in improving robot control performance. In this paper, a virtual sensor based on the total generalized momentum concept is proposed to estimate the total friction torque, including both the motor-side and link-side friction, of [...] Read more.

Robot joint friction is an important and complicated issue in improving robot control performance. In this paper, a virtual sensor based on the total generalized momentum concept is proposed to estimate the total friction torque, including both the motor-side and link-side friction, of robot joints without joint torque sensors. The proposed algorithm only requires a robot joint dynamics model and not a complex friction model dependent on factors such as time and velocity. By compensating for the estimated friction torque with a robot joint controller, the trajectory tracking performance of the controller, especially the velocity tracking performance, can be improved. To verify the effectiveness of the developed algorithm, 2-DOF planar manipulator simulations and single-joint system experiments are conducted. The simulation and experimental results show that the designed virtual sensor can effectively estimate the total joint friction disturbance and that the controller trajectory tracking performance is improved after observed friction compensation. However, the position tracking performance improvement of the controller is less than that for the velocity tracking performance improvement during the experiments. In addition, the velocity step response ability and velocity tracking performance of the controller are improved more at low velocities than that at high velocities in the experiments. The proposed algorithm has engineering and theoretical significance for estimating robot joint friction and improving the performance of robot joint controllers. Full article

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20 pages, 1169 KiB

Open AccessArticle

Multi-Constraint Optimized Planning of Tasks on Virtualized-Service Pool for Mission-Oriented Swarm Intelligent Systems

by Kailong Zhang, Chao Fei, Baorong Xie, Yujia Wang, Zheng Gong, Chenyu Xie, Thi Mai Trang Nguyen, Yuan Yao and Kejian Miao

Appl. Sci. 2019, 9(15), 3010; https://doi.org/10.3390/app9153010 - 26 Jul 2019

Cited by 2 | Viewed by 3004

Abstract

With the emergence of swarm intelligent systems, especially the swarming of aircraft and ground vehicles, cooperation in multiple dimensions has becoming one of the great challenges. How to dynamically schedule the resources within a swarm intelligent system and optimize the execution of tasks [...] Read more.

With the emergence of swarm intelligent systems, especially the swarming of aircraft and ground vehicles, cooperation in multiple dimensions has becoming one of the great challenges. How to dynamically schedule the resources within a swarm intelligent system and optimize the execution of tasks are all vital aspects for such systems. Focusing on this topic, in this paper, one new task planning mechanism with multiple constraints is proposed to solve such dynamic programming problems. Concretely, several fundamental models, covering three-level task models and resource-service pool models, are put forward and defined first. Considering the limitations of swarm systems running within complicated cyber-physical space, multi-dimension constraints for tasks scheduling and execution are further modeled and established. On this basis, we mapped this planning problem to an optimization searching problem, and then proposed a Genetic-Algorithm-based mechanism. All these works have been verified with simulated cooperation scenes. Experimental results show that this new mechanism is efficient to solve such resource-related and mission-oriented cooperation problems in complicated environments. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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15 pages, 6953 KiB

Open AccessArticle

A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm

by Peng-Hao Hu, Ze-Xun Lu, Yuan-Qi Zhang, Shan-Lin Liu and Xue-Ming Dang

Appl. Sci. 2019, 9(14), 2850; https://doi.org/10.3390/app9142850 - 17 Jul 2019

Cited by 8 | Viewed by 2978

Abstract

The rotation orientation and the angle of precision of an intelligent ball joint cannot be automatically obtained in passive motion. In this paper, a new method based on a Hall sensor with a permanent magnet (PM) is proposed to identify the spatial rotation [...] Read more.

The rotation orientation and the angle of precision of an intelligent ball joint cannot be automatically obtained in passive motion. In this paper, a new method based on a Hall sensor with a permanent magnet (PM) is proposed to identify the spatial rotation orientation and angle. The basic idea is to embed a PM on a ball while the Hall sensors are arrayed into the ball socket. When the ball rotates, the Hall sensor array detects the variation of the magnetic induction intensity in space. By establishing a mathematical model between the variation of the magnetic induction intensity and the orientation and angle of rotation, the rotation angle in the space where the ball is located can be inversely solved. The establishment of the theoretical model is based on the theory of the equivalent magnetic charge method, which has a few native defects that cannot be overcome by itself. This paper presents the relationship between the magnetic induction intensity change and the rotation angle of the ball in space, which was constructed by an artificial neural network (ANN) and will simplify the mathematical model, shorten the operation time, and improve the efficiency of real-time detection. Based on the simulation analysis, the optimal matching scheme between the PM and the magnetic effect sensor was determined, and the structural parameters of the ball joint prototype were optimized. The data training and comparison test of the neural network model were completed on a self-developed calibration device. The experimental results show that for a ±20° measurement range, the average errors of the uniaxial measurements are 1′51″ and 1′55″ on the two axes, respectively. At present, the measurement accuracy of the prototype is still relatively low; however, this idea of modeling based on ANN removes the shackles of mathematical modeling, reminding us that we can consider the design of sensors or complete geometric measurement modeling from a new perspective. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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30 pages, 8927 KiB

Open AccessArticle

An Analytical Design of Simplified Decoupling Smith Predictors for Multivariable Processes

by Vo Lam Chuong, Truong Nguyen Luan Vu, Nguyen Tam Nguyen Truong and Jae Hak Jung

Appl. Sci. 2019, 9(12), 2487; https://doi.org/10.3390/app9122487 - 18 Jun 2019

Cited by 16 | Viewed by 3172

Abstract

In this study, the issues of complicated interactions between process variables were solved by decoupling techniques; in particular, simplified decoupling was used due to its simplicity and robustness. A new approach to solving decoupling realizability was developed by using the modified particle swarm [...] Read more.

In this study, the issues of complicated interactions between process variables were solved by decoupling techniques; in particular, simplified decoupling was used due to its simplicity and robustness. A new approach to solving decoupling realizability was developed by using the modified particle swarm optimization (PSO) algorithm. However, time delays still existed in the diagonal elements of the decoupled matrix, and they resulted in a more sophisticated controller design and sluggish responses in the outputs. To overcome the adverse effects of time delays, a Smith predictor, also known as a dead time compensator, is normally used. In this work, a Smith predictor structure in combination with simplified decoupling for multivariable processes was proposed in order to enhance system performances in terms of the servomechanism problem. The proportional integral or proportional integral derivative (PI/PID) controller tuning rules for several common industrial processes, such as first-order, second-order, and second-order with negative zero systems, were obtained. Many multivariable industrial processes were adopted to simulate the effectiveness of the proposed method in terms of the servomechanism problem and robust response. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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Review

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29 pages, 4784 KiB

Open AccessReview

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

by Jianjun Ni, Yinan Chen, Yan Chen, Jinxiu Zhu, Deena Ali and Weidong Cao

Appl. Sci. 2020, 10(8), 2749; https://doi.org/10.3390/app10082749 - 16 Apr 2020

Cited by 104 | Viewed by 20450

Abstract

Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in [...] Read more.

Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)

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