International Journal of Wireless and Mobile Computing (25 papers in press)
Expected-mode augmentation method for group targets tracking using the random matrices
by Yun Wang, Guoping Hu, Hao Zhou
Abstract: In order to improve the estimation performance of interactive multiple models (IMM) tracking algorithm for group targets, a new EMA-VSIMM tracking algorithm is proposed in this paper. Firstly, by using the expected-mode augmentation (EMA) method, a more proper expected mode set has been chosen from the basic model set of group targets, which can make the selected tracking models match up to the unknown true mode availably. Secondly, in the filtering process of variable-structure interactive multiple model (VSIMM) approach, the fusion estimation of kinematic state and extension state have been implemented by using classical weighting method and scalar coefficients weighting method, respectively. We use the trace of the corresponding covariance matrix of extension state to calculate the weight coefficient. We calculate the prediction value of the extension state parameter by using a fuzzy reasoning approach to improve the estimation accuracy of the covariance matrix, which takes the elliptical area of extension and its change ratio as the input of the fuzzy controller. The performance of the proposed EMA-VSIMM algorithms is evaluated via simulation of a generic group targets manoeuvring tracking problem.
Keywords: interactive multiple models; expected-mode augmentation; group targets; maneuvering tracking.
Low financial cost with ant colony optimisation in intelligent agriculture
by Xu Gaofeng
Abstract: With the development of wireless sensor networks, industrial automation and other computer and information related high technologies, a lot of practical IoT applications have greatly increased the productivity. Currently, more and more capital is being invested in IoT, especially intelligent agriculture as many countries begin to pay more attention to basic and intelligent agriculture. For large intelligent agriculture systems, it will cost a lot of time and energy (which further will cost investors' money) for the mobile sink to collect all the data of the sensing system with the help of cluster head node. In this paper, we try to solve this issue that minimizes the data collection path of the mobile sink, with the help of the ant colony optimisation algorithm. We implement the algorithm in Python and conduct two experiments that show that we can get the best path of the given example and show how the efficiency changes when the numbers of ants and loops increase. The better the optimal path becomes, the less financial cost we can achieve.
Keywords: financial cost; wireless sensor network; ant colony optimisation; intelligent agriculture.
Demand estimation of water resources via bat algorithm
by Xiangdong Pei, Youqiang Sun, Yeqing Ren
Abstract: In the process of urban water resources planning, the demand estimation of urban water consumption is one of the important basic contents. In this paper, a hybrid of a linear estimation model and an exponential estimation model is proposed to forecast the water consumption. The bionic intelligent algorithms are widely used in industrial engineering, so we use intelligent algorithms to solve the proposed model including Bat Algorithm (BA) and modified Bat Algorithm (FTBA). FTBA improves the global search capability, and the improvements increase the probability of solving the optimal value. In the simulation experiments, we use the data from Nanchang city during 2003 to 2015. The data from 2003 to 2012 are used to find the optimal weights, and the remaining data (2013-2015) are used to test the model. Simulation results show that the modified BA (FTBA) is superior to the standard algorithm and achieves higher accuracy in prediction.
Keywords: demand estimation; water resource; hybrid model; bat algorithm.
Particle swarm optimisation with multi-strategy learning
by Guohan Lin, Jing Sun
Abstract: To ease the conflict between diversity and convergence rate encountered by PSO, a multi-strategy learning particle swarm optimisation algorithm (Multi-strategy Learning PSO, MSLPSO) is proposed. The proposed method can effectively preserve the heuristic information, a modified differential mutation is combined with PSO to expand the search range and to increase the diversity of the population. If the population is trapped into local optimum, the inferior particle adopts opposition-based learning, This mechanism can improve the diversity and can help the particles to move away from the local optimum. Gaussian disturbance is applied to elite particle to further improve the diversity of particle and to the proposed MLSPSO's exploration ability. Twelve benchmark function tests from CEC2005 are used to evaluate the performance of the proposed algorithm. The results show that the proposed multi-strategy learning has performed consistently well compared with other state-of-art PSO algorithms
Keywords: particle swarm optimisation; learning strategy; differential mutation; perturbation strategy; numerical optimisation.
Performance analysis of the IEEE 802.15.4e TSCH-CA algorithm under a non-ideal channel
by Soraya Touloum, Louiza Bouallouche-Medjkoune, Djamil Aissani, Celia Ouanteur
Abstract: Recently, the IEEE 802.15.4e amendment has developed a new MAC behaviour mode named Time Slotted and Channel Hopping (TSCH) to support the Industrial Wireless Sensor Networks (IWSNs) requirements. TSCH combines time slots with channel hopping and defines shared and dedicated links. A shared link is attributed to more than one sender which leads to collisions. To decrease the probability of repeated collisions in the packet retransmission, the TSCH Collision Avoidance (TSCH-CA) algorithm has been implemented by the 802.15.4e amendment. This paper proposes a two-dimensional Markov chain model to evaluate the performances of the TSCH-CA algorithm when only shared links are used under non-ideal channel conditions. The accuracy of this model has been verified through Monte Carlo simulations. Based on the proposed model, the expressions of different performance metrics that include retransmission probability, data packet loss rate, reliability, energy consumption, normalised throughput and average access delay have been obtained. Furthermore, a comparative study between TSCH-CA and the unslotted CSMA-CA of IEEE 802.15.4 under a non-ideal channel has been provided. Numerical results reveal that the TSCH-CA performances are clearly affected by channel errors when using only shared links under a noisy environment.
Keywords: IWSNs; IEEE 802.15.4e; TSCH-CA; non-ideal channel; modelling; Markov chains; performance analysis.
Performance of UWB communication systems in the presence of perfect/imperfect power control MAI and IEEE802.11a interference
by Ehab Moustafa Shaheen
Abstract: This paper evaluates the bit error rate performance of ultra-wideband (UWB) communication system under the impact of both multiple access and narrowband interferences operating. The narrowband interference (NBI) signal is modelled as the IEEE802.11a orthogonal frequency division multiplexing based wireless local area network signal, which can be approximated by the sum of independent asynchronous tone interferers with arbitrary frequencies. Multiple access interference (MAI) is assumed a zero mean Gaussian process, where it has been investigated in both perfect and imperfect power control scenarios. The bit error rate performance is evaluated in three different channel models: ideal channel model (additive white Gaussian noise channel), Nakagami-m multipath fading channel model and the IEEE802.15.3a UWB channel model. It is shown that the performance of a UWB system is severely degraded owing to the presence of both types of interference, yet the NBI has more impact on the performance of UWB communication systems compared with the multiple access one.
Keywords: impulse radio ultra-wideband; narrow band interference; IEEE802.11a; multiple access interference; Nakagami-m multipath fading channel; IEEE802.15.3a UWB channel model.
Quality of experience prediction model for video streaming in SDN networks
by Abar Tasnim, Benletaifa Asma, Sadok El Asmi
Abstract: To get an idea of the quality of a network, the majority of stakeholders (network operators, service providers) rely on quality of service. This measure has shown limits and a great deal of effort has been put into putting in place a new metric that more accurately reflects the quality of service offered. This measure is known as quality of experience (QoE). The QoE reflects the user's satisfaction with the service. Today, evaluating the QoE has become paramount for service providers and content providers. This necessity has pushed us to innovate and to design new methods to estimate the QoE. Our work in this paper comprises two parts: the first part defines our subjective method which evaluates the quality of video streaming over SDN networks, according to the principle of DCR (Degradation Category Rating), and study the effect of the QoS parameters (packet loss, delay, bandwidth), application parameters (resolution, bit rate, frame rate) on user perception MOS (mean opinion score). In the second part we try to cover the impairments of subjective methods since there are expensive, take a lot of time and not in real time by novel method that predicts the quality of experience MOS based on machine learning, so we employ 3 types of classifiers (decision trees, meta classifiers, functions classifiers) with different k-fold cross-validation then we calculate RMSE, r, MAE, RRSE to measure the performance of each algorithm to deduce the best one. After the analysis, we obtain when k = 9, M5P and Random forest are the best algorithms in our model, which helps us to predict the perception of the user.
Keywords: SDN; machine learning; MOS.
A hybrid algorithm for secure cloud computing
by Debasis Das
Abstract: Cloud computing is a developing technology that is yet unclear to many security issues. Data in the untrusted clouds can be encrypted using an encryption algorithm. Randomising this data provides more security which can be achieved by padding concept in the cloud. In this paper, the users data is encrypted using padding scheme, called Optimal Asymmetric Encryption Padding (OAEP), together with Hybrid Encryption algorithm that is based on RSA (i.e., HERSA), to allow multiple parties to compute a function on their inputs while preserving integrity and confidentiality. The homomorphic encryption (HE) is performed on the encrypted data without decrypting it in computationally powerful clouds, and the Secure Multi-Party Computation (SMPC) can be used in the cloud to ensure security and privacy of the users. In this paper, we propose a scheme that integrates the multi-party computation with HE to allow calculations of encrypted data without decryption. The cryptographic techniques used in our cloud model are described, and the overheads are compared with HE and multi-party computation.
Keywords: cloud computing; optimal asymmetric encryption padding; homomorphic encryption; multiparty computation.
Multi-agent list-based noising algorithm for protein structure prediction
by Juan Lin, Yiwen Zhong, ENa Li
Abstract: Protein structure prediction (PSP) with AB initio model problem is a challenge in bioinformatics on account of high computational complexity. To solve this problem within a limited time and resource, a multi-agent list-based noising (MLBN) algorithm is presented. MLBN contains three main features. First, a flexible noising list is designed to adjust the solution acceptance condition according to the convergence. An adaptive multiple sampling strategy is included to provide a strong exploitation. A parallel framework explores the searching space in a more effective way. Compared to traditional simulated annealing algorithm, MLBN introduces only one extra parameter for the length of noising list and it is insensitive to specific problems. Experiments conducted with a range of protein sequences indicate that MLBN performs better than, or at least comparably with, several state-of-the-art algorithms for PSP.
Keywords: noising method; list-based adaptive sampling; protein structure prediction; multi-agent.
A new way of achieving multipath routing in wireless networks
by Abd El Djalil Temar, Mustapha Geuzouri, Nader Mbarek
Abstract: In the 21st century, wireless technology is still developing rapidly and trying to be 'faster, higher, and stronger': faster data rate, higher bandwidth and stronger connectivity. Wireless Mesh Networks (WMN) have been envisioned as an important solution to the next generation wireless networking which can be used in wireless community networks, wireless enterprise networks, transportation systems, home networking and last-mile wireless internet access. They also provide a cheap, quick and effective way for building wireless data networks. Considering the nature of these networks, routing is a key process for operating the WMN. This paper proposes a new way of creating multipath routing protocols based on a fusion between multicast and unicast routing protocols to get different routes (node-disjoint or link-disjoint) from the source to its destination. The simulation results using Network Simulator 2 (NS-2) show that our new Multipath Routing Protocol (Fusion) outperforms Ad hoc On-Demand Distance Vector (AODV) and Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocols in terms of average network throughput, end-to-end delay, and number of flows.
Keywords: wireless mesh networks; routing; multipath routing protocols; multicast and unicast routing protocols; throughput; end-to-end delay.
Sub-word attention mechanism and ensemble learning-based semantic annotation for heterogeneous networks
by Liang Zhang, Zhaobin Liu, Jinxiang Li, Gang Liu, Yuanfeng Yang, Yi Jin, Xu Zhang
Abstract: The sensing device and wireless sensor networks (WSN) can provide information to the application of Internet of Things (IoT), but tens of thousands of different types of sensing device and the obtained data present significant polyphyly and heterogeneity, which poses challenges to the collaboration and interaction of information resources in IoT applications and services. It is difficult to unify the identification of different types of wireless sensing device and their data, especially when the device uses Chinese characters for information and knowledge representation. This paper introduces an ensemble learning model based on sub-word attention mechanism and bidirectional long short-term memory model (SWAT-Bi-LSTM) which can provide an internal structural attention ability of Chinese characters. The experimental results show that compared with the method without using the internal structural attention mechanism of Chinese characters, this method can effectively improve the accuracy of sentiment analysis, and the integrated learning model can further improve the accuracy and stability.
Keywords: sentiment analysis; sub-word units; Bi-LSTM; ensemble learning; wireless sensor networks.
Bimanual gesture recognition based on convolution neural network
by Hao Wu, Gongfa Li, Ying Sun, Guozhang Jiang, Du Jiang
Abstract: Gesture recognition is a key research field in human-computer interaction. At present, most researchers focus on one-handed gesture recognition, but do not pay much attention to bimanual (two hands) gesture recognition. This paper presents a deep learning-based solution to tackle the self-occlusion and self-similarity. To solve this problem, this paper uses Kinect to collect many colour and depth images of different gestures, and each gesture contains multiple sample individuals. Colour images and depth images are used to train the recognition model of bimanual gesture, and then fused the colour image and depth image, and train the bimanual gesture recognition model based on colour image and depth image fusion. Then the bimanual recognition effects of three models are compared. The experimental results show that, regardless of the single gesture precision or the mean average precision, the bimanual gesture recognition effect of the fused model is better than the gesture recognition models based on colour image or depth image.
Keywords: gesture recognition; bimanual gesture; deep learning; CNN; occlusion.
A content-independent domain abuse detection method
by Fan Yang, Zhengrong Xiang, Shoulian Tang
Abstract: This paper proposes a series of language-independent domain name abuse detection features, including domain name string features, domain name registration features, domain name resolution features and domain name service features, and trains six pattern recognition algorithms in the corresponding feature space. To validate the effectiveness of extracted features and leaning algorithms, a practical dataset is constructed, and the performances of related features and learning algorithms are compared and analysed. The experimental results show that the multi-scale features extracted have good recognition ability. The proposed language-independent domain name abuse detection method can effectively cover multiple types of abuse and is easy to implement. It is applicable to the pre-steps of global DNS services and web content services, etc., which can not only effectively save bandwidth, computing and storage resources, but also effectively improve the stability and efficiency of related services.
Keywords: domain name system; domain abuse detection; machine learning; feature extraction.
A framework for controlling the operations of sensor networks from the cloud
by Khaleel Mershad
Abstract: Wireless sensor networks (WSN) have evolved as one of the most important research topics in the last decades. A new paradigm of combining WSN with cloud computing has led to what is called Sensor-Cloud, in which the cloud capabilities are used to manage the sensors that are scattered throughout the WSN. In this paper, we propose a new approach for managing the various operations of a WSN from cloud data centres. Our system enables WSN administrators to control the behaviours and operations of sensor nodes in a WSN, such as data-gathering frequency, sleep mode duration, and node mobility, whenever required. Our system benefits both the WSN user, who can express his requirements and obtain his data more accurately and efficiently, and the WSN administrator or manager, who ensures the best network performance. We implemented our system as a new protocol in the Network Simulator 2 (NS2) software, and tested the system performance by executing different scenarios and measuring several parameters, such as response time, throughput, and energy consumption.
Keywords: wireless sensor network; cloud computing; cloud services; network management; data gathering; sensor mobility.
Delay threshold scheduling algorithm for LTE downlink systems
by Fu-Min Chang, Hsiu-Lang Wang, Po-Hsueh Wang, Shang-Juh Kao
Abstract: Modified Largest Weighted Delay First (M-LWDF), Exponential Proportional Fair (EXP/PF), and Channel Dependent Earliest Due Date (CD-EDD) are among the algorithms most commonly referenced for the scheduling of requests in real-time multimedia applications using LTE downlink systems. These methods take into account channel conditions as well as packet delays in the dispatch of packets. However, when using M-LWDF, a failure to prioritise packets approaching their delay budget may lead to their exclusion. Conversely, when using CD-EDD or EXP/PF, packets with good channel conditions and short wait times may not be scheduled due to selection criteria emphasising packets that are closest to expiration. This paper proposes a delay threshold scheduling (DTS) algorithm that takes into account channel conditions as well as the packet waiting time. Delay thresholds were adopted to enable the differentiation of packets in conjunction with proportional weighting between channel conditions and packet waiting time for the prioritisation of real-time services. Simulation results demonstrate that compared to the results obtained using M-LWDF, EXP/PF, and CD-EDD, the proposed DTS algorithm is able to increase video traffic throughput and decrease packet delays and the packet loss ratio for real-time services.
Keywords: downlink scheduling; delay threshold; channel condition; packet waiting time.
Multicast stable path routing protocol for wireless ad-hoc networks
by K.S. Saravanan, N. Rajendran
Abstract: Wireless Ad-Hoc Networks (WANETs) enable steady communication between moving nodes through multi-hop wireless routing path. The problem identified is how to improve the lifetime of the route and reduce the need for route maintenance. This helps to save bandwidth and reduce the congestion control available in the network. This paper aims to focus on redesign and development of multicast stable path routing protocol with special features that determine long-living routes in these networks. An extensive ns-2 simulation based performance has been analysed of three widely recognised stability oriented wireless ad-hoc network routing protocols, namely are Associativity Based Routing (ABR) protocol, Flow Oriented Routing Protocol (FORP) and Lifetime Route Assessment Based Routing Protocol (LRABP). The order of ranking of the protocols in terms of packet delivery ratio, average hop count per route, end-to end delay per packet and the number of route transitions is presented.
Keywords: wireless ad-hoc networks; multicast routing protocol; wireless communication; routing protocol.
A new optimised interleaver design for high dimensional data transmission in the SCM-OFDM system
by Rashmi Swamy, Mrinal Sarvagya
Abstract: Superposition Coded Modulation (SCM) is considered as an alternative approach for transmitting data with increased throughput. It has numerous benefits over traditional CM approaches. The OFDM method has attained vast attention, since it permits a spectrally efficient transmission at acceptable execution cost. The drawbacks of the most widespread SCM-Orthogonal Frequency Division Multiplexing (SCM-OFDM) include high receiver complexity. Hence, this paper intends to develop a new interleaver design for the SCM-OFDM system. Initially, a set of data is transmitted through the SC-OFDM system, for different Signal-to-Noise Ratios (SNR). In the interleaver of SCM-OFDM, the scramble rule generation is considered as the challenging point, which needs to be optimised to make the system more effective by arranging data in a non-contiguous way. Accordingly, the optimal scramble generation is introduced in this paper using the hybridisation of Rider Optimisation Algorithm (ROA) and Group Search Optimisation (GSO) algorithms. The new hybrid algorithm is termed as GSO Bypass based ROA (GB-ROA). In the receiver side, the same optimised scramble rules is used in the deinterleaver, and mean error is computed between the transmitted and received data, considering the entire allotted data. The iterative process of scramble rule generation is repeated until the mean error reaches a minimum. Moreover, the presented model is compared with conventional schemes, and the outcomes are attained through Bit Error Rate (BER) and Mean Square Error (MSE) analysis.
Keywords: SCM-OFDM system; interleaver; scramble rule generation.
Performance improvement of energy detector in cognitive radio using SECp diversity combining technique over fading channel
by Rupali Agarwal, Himanshu Katiyar, Neelam Srivastava
Abstract: In this paper, the performance of an energy detector is analysed in Rayleigh fading environment using different diversity combining techniques. The channel is considered as independent and identically distributed (IID). The closed form expressions for probability of detection using switch and stay combining (SSC) as well as switch and examine combining using post-examining selection (SECp) are derived. The performances are compared using the curves of probability of detection for different signal to noise ratios. Probability of false alarm vs probability of misdetection curves are also compared (SNR). These curves are called complementary receiver operating characteristics (ROC) curves. SECp is a modified form of switch and examine combining (SEC) scheme. Unlike SEC, when all the paths are tested and none of them has an acceptable SNR, the SECp combiner selects the path with the highest SNR. So the performance of SECp is improved, which is shown with the help of both the graphs.
Keywords: probability of detection; cognitive radio; switch and stay combining; switch and examine combining with post-examining selection; complementary ROC; probability of false alarm; probability of misdetection.
Container keyhole positioning based on deep neural network
by Li Yan, Fang Juanyan
Abstract: In recent years, more and more automated container ports have increased the requirements for the accuracy and real-time performance of container keyhole identification. In this paper, the improved deep neural network algorithm YOLO is used to identify the position of the keyhole. Compared with the original algorithm, the input image is reduced to a grayscale image, and the number of prediction grids used for detection is reduced from 13*13 to 11*11. The second positioning of the target area is carried out, and the keyhole identification is achieved under different lighting conditions and complex background. Compared with the original method, this method increases the dimensionality reduction of the input vector and the accurate extraction of the subsequent target area, shortens the detection time and improves the accuracy. Specifically, the detection time is reduced by 22 ms and the precision is improved by 4%. The model trained in this paper has the mean average precision of 87.7% under the test set, the accuracy rate of 96%, the recall rate of 83%, and an intersection-over-union of 80.43%. The detection time of an image on GPU is 10 ms, the detection time on CPU is 80 ms, and the frame rate of the actual detected video reaches 15 FPS. This study provides a theoretical basis for automatic positioning of container keyholes.
Keywords: deep learning; container keyhole; target identification; you only look once; YOLO.
New strategy for resource allocation using PSO-PFS hybrid
by Zenadji Sylia, Gueguen Cédric, Brikh Lamine, Talbi Larbi, Khireddine Abdelkrim
Abstract: In this paper, the problem of resource allocation in a tri-sectorial cell taking into account the OFDM transmission technique used in wireless network, such as LTE, and the upcoming 5G, is studied. A new strategy for resource allocation scheme based on PSO-PFS hybrid (Particle Swarm Optimization and Proportional Fair Scheduling) is proposed to help the users who are in a critical location and to have an optimal distribution of resources. The scheme leverages the PFS algorithm taking into consideration the channel state conditions of the users, where the PSO algorithm provides an optimal solution to the allocation problem and improves the performances of users according to their channel state. The simulation results show that the allocation of resources by the PSO-PFS hybrid guarantees a high throughput of the system by ensuring fairness through all users.
Keywords: tri-sectorial cell; OFDM modulation; LTE; 5G; PFS; PSO.
Novel fountain data estimation scheme by exploiting Bayesian model classification in wireless sensor networks
by Fatma Belabed, Ridha Bouallegue
Abstract: The crucial goal of fountain codes is to reduce the number of transmissions as well as the use of a feedback channel. The roll-out of these codes is limited by multi-hops transmission. Indeed, with the multi-hops transmission, fountain codes raise the problem of overflow leading to a waste of energy, the most critical issue and the big challenge in WSN. The number of encoded packets generated is significantly reduced and the residual energy can be preserved by using a clustered architecture and classification technique. In this paper, we consider a distributed estimation scheme composing of a sensor member and a fusion centre. In order to reduce the number of useless encoded packets and consequently the number of transmissions, we determine the number of encoded packets needed to recover sent data. We adopt fountain codes for data encoding and then packets are assembled at the cluster head (CH). Each CH provides a final estimation using a classification within Bayes rules. We prove the power of emergence of fountain codes and training machine learning models to exactly calculate the needed number of encoded packets and to preserve residual energy significantly improves.
Keywords: Wireless Sensor Networks; Fountain codes; Data Estimation; Bayes Rule; Naive Bayes.
Anti-swing strategy of overhead cranes based on prescribed performance PID control
by Xianghua Ma, Zhenkun Yang, Wenjie Li, Gang Wu, B.I.N. WEI
Abstract: As a widely used underactuated system, overhead cranes have been extensively studied. Although there are already a lot of research results, it is difficult to use computers as controllers due to the complicated application environment, especially in steel plants and docks. In harsh environments such as high humidity, in order to overcome such limitations while keeping the swing angle within an acceptable range or even zero, intelligent control algorithms based on traditional Lagrangian methods are difficult to implement on PLC or embedded systems. Although the traditional PID control can solve the problem of robust adaptive and disturbance rejection very well, the quality of PID parameters depends too much on the experience of adjusting parameters or requires a lot of trial and error, so a new nonlinear PID control method is proposed in this paper. The theory of prescribed performance control is introduced, and a nonlinear closed-loop PID controller is designed based on the idea of performance function and error conversion of prescribed performance control. And the parameter adjustment is more flexible, the system has better robustness, adaptability and immunity to disturbance. It effectively realizes the precise positioning of overhead crane and anti-rolling of crane, and has excellent anti-interference ability.
Keywords: overhead crane system; anti-swing closed-loop control; nonlinear closed-loop PID controller; prescribed performance.
Research on preventive maintenance strategy of multi-equipment system based on the internet of things
by Guo-chen Zhang, Hui Shi, Zhaobo Chen, Xiaobo Li
Abstract: Maintenance activities of equipment are closely related to the condition of equipment and the production demand in the production system consisting of multi-equipment. The operational status of equipment determines the preventive maintenance scheme. To satisfy the maximum production demand, this research combines the internet of things technology to monitor the operational status of multi-equipment in real time. Based on this, we put forward the strategies for the preventive maintenance (PM) of independent multi-equipment system by proposing the thresholds such as optimum PM interval, the minimum PM interval and maximum postponement period of PM. In order to minimise the production loss and maintenance cost of a manufacturing system by optimisation, this research constructed a production demand based PM scheduling model. Then, a genetic algorithm based simulation optimisation was used to conduct the case solution. Results show that the strategies proposed are able to effectively coordinate the PM activities of multi-equipment, which is under the real time condition monitoring, in the case of limits of production demand.
Keywords: internet of things; independent multi-equipment system; data sharing; preventive maintenance; condition monitoring.
Chinas research and prospect on discursive power of ideological and political education in internet environment
by Qiong Li, Jianqing Ma
Abstract: In the new era of socialism with Chinese characteristics, the value orientation of the discursive power of ideological and political education in the internet environment aims to construct a network system of ideological and political education with Chinese characteristics. To master the discursive power of ideological and political education in the internet environment, we should base ourselves on the dual connotation of the culture and the rule of law of discursive power, and conduct innovative explorations at the practical level. A comprehensive and systematic overview of the value orientation, actual connotation and realisation mechanism of the discursive power of ideological and political education in the internet environment is helpful to provide a reference for the further development of the discursive power of ideological and political education.
Keywords: discursive power; value orientation; cultural connotation; legal connotation; practice innovation.
On optimisation of a web crawler system on the scrapy framework
by Kaiying Deng, Senpeng Chen, Jingwei Deng
Abstract: With the continuous development of internet technology, life is accompanied by data at all times. However, network data is so complicated and confusing that it has become difficult for users to find valuable information. Therefore, being able to acquire data from a vast data ocean has become an essential skill for today's business development. In this paper, a web crawler system based on the scrapy framework is optimised to further enhance the crawler efficiency, increase the crawler speed, and break the crawler limit.
Keywords: network data; scrapy framework; web crawler; optimisation.