Remote monitoring of an omnidirectional smoke detection system using texture features image processing techniquesPeng, Chun-Chi ; Hsu, Kuei-Shu ; Her, Ming-Guo ; Peng, Yen-Chia ; Jiang, Jinn-Feng ; Chen, Yi-Jie
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0256
Purpose – The purpose of this paper is to develop an early fire-alarm raising system based on video processing, and combine it with the omnidirectional projecting system. It not only gives alarm immediately in early fire so that people can be able to strive for more time to escape from the spot, but also solves problem of discontinued screen which was presented fire scene. Design/methodology/approach – The smoke detection system is made by image processing. The flowchart of smoke detection is improved, which the method of background updating can filter out the moving objects that only stay for a short time in the image; and avoids these objects being determined to be the background. Moreover, the authors extract the flickering area to separate the non-smoke and smoke from the candidate of smoke regions. Finally, the image processing is applied in omnidirectional projecting system, then presented the 360-degree fire scene. Findings – The results show that the smoke detection system can accurately detect the smoke and mark its location, then combining it with the omnidirectional projecting system, although the resolution of omnidirectional projecting system is not enough, it can present a continued screen and location of smoke on the 360-degree cylindrical screen. Originality/value – This paper develops the smoke detection based on a improved method of image processing, and the control center staff can see the 360-degree fire scene via omnidirectional projecting system, so shorten the time to find the source of smoke.
Greedy immunization strategy in weighted scale-free networksLiu, Zhang-Hui ; Chen, Guo-Long ; Wang, Ning-Ning ; Song, Biao
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0285
Purpose – The purpose of this paper is to present a new immunization strategy for effectively solving the control of the spread of the virus. Design/methodology/approach – Inspired by the idea of network partition, taking two optimization targets which are the scale of sub-network and the sum of the strengths of the sub-network's nodes into account at the same time, a new immunization strategy based on greedy algorithm in the scale-free network is presented. After specifying the number of nodes through the immunization, the network is divided into the scale of sub-network and the sum of the strength of the sub-network's nodes as small as possible. Findings – The experimental results show that the proposed algorithm has the better performance than targeted immunization which is supposed to be highly efficient at present. Originality/value – This paper proposes a new immunization strategy based on greedy algorithm in the scale-free network for effectively solving the control of the spread of the virus.
Shortest distance estimation in large scale graphsChen, Yuzhong ; Yu, Yang ; Chen, Guolong
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0286
Purpose – Shortest distance query between a pair of nodes in a graph is a classical problem with a wide variety of applications. Exact methods for this problem are infeasible for large-scale graphs such as social networks with hundreds of millions of users and links due to their high complexity of time and space. The purpose of this paper is to propose a novel landmark selection strategy which can estimate the shortest distances in large-scale graphs and clarify the efficiency and accuracy of the proposed strategy in comparison with currently used strategies. Design/methodology/approach – Different from existing strategies, the landmark selection problem is regarded as a binary combinational optimization problem consisting of two optimization objectives and one constraint. Further, the original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without any additional constraints and the equivalence of solutions is proved. Finally the solution of the optimization problem is performed with a modified multi-objective particle swarm optimization (MOPSO) integrating the mutation operator and crossover operator of genetic algorithm. Findings – Four real networks of large scale are used as data sets to carry out the experiments and the experiment results show that the proposed strategy improves both of the accuracy and time efficiency to perform shortest distance estimation in large scale graph compared to other currently used strategies. Originality/value – This paper proposes a novel landmark selection strategy which regards the landmark selection problem as a binary combinational optimization problem. The original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without constraints and the equivalence of these two optimization problems is proved. This novel strategy also utilizes a modified MOPSO integrating the mutation operator and crossover operator of genetic algorithm.
Assessing the effects of audio-visual stimulation on the prefrontal EEG of good & poor sleepersLee, Yi-Yeh ; Raymond See, Aaron ; Chen, Shih-Chung ; Liang, Chih-Kuo
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0287
Purpose – The purpose of this paper was to investigate the response of good and poor sleepers toward audio-visual stimulation via prefrontal theta EEG measurement. Design/methodology/approach – The experiment included ten healthy subjects that were chosen after going through the Pittsburgh Sleep Quality Index (PSQI). They were divided into two groups that include five good and five poor sleepers. Next, in order to clarify the effects of audio-visual biofeedback during daytime, each subject was asked to go through six two-minute tasks that include: pre-baseline, eyes open at rest, eyes closed at rest, audio biofeedback with eyes open, video biofeedback also with eyes open, and post-baseline. Findings – In Task 4, the audio stimulation task, both types of sleepers elicited higher theta waves due to demand in mental activity and also a meditation state. It was significantly higher in poor sleeper that demonstrated a peak difference of 25 percent compared to its good sleeper counterpart. In Task 5, the visual stimulation task, through the use of random numbers having blue and red color background, the theta amplitudes of good and poor sleepers drop together, due to beta waves becoming dominant, as the task required attention and focussed accounting for reduced theta amplitudes. The study was able to prove the use of prefrontal EEG in measuring and evaluating sleep quality by examining theta variation. Originality/value – This paper proposed a novel and convenient method for evaluating sleep quality by utilizing only a single channel prefrontal EEG measurement.
Improved initial cluster center selection in K-means clusteringZhu, Minchen ; Wang, Weizhi ; Huang, Jingshan
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0288
Purpose – It is well known that the selection of initial cluster centers can significantly affect K-means clustering results. The purpose of this paper is to propose an improved, efficient methodology to handle such a challenge. Design/methodology/approach – According to the fact that the inner-class distance among samples within the same cluster is supposed to be smaller than the inter-class distance among clusters, the algorithm will dynamically adjust initial cluster centers that are randomly selected. Consequently, such adjusted initial cluster centers will be highly representative in the sense that they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can then be effectively reduced. In addition, the algorithm is able to obtain all initial cluster centers simultaneously (instead of one center at a time) during the dynamic adjustment. Findings – Experimental results demonstrate that the proposed algorithm greatly improves the accuracy of traditional K-means clustering results and, in a more efficient manner. Originality/value – The authors presented in this paper an efficient algorithm, which is able to dynamically adjust initial cluster centers that are randomly selected. The adjusted centers are highly representative, i.e. they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can be effectively reduced so that the authors can achieve an improved clustering accuracy. In addition, the algorithm is a cost-efficient one and the enhanced clustering accuracy can be obtained in a more efficient manner compared with traditional K-means algorithm.
A neural observer for sensorless speed control of servomotorsHorng, Jenq-Ruey ; Wang, Ming-Shyan ; Lai, Tai-Rung ; Berinde, Sergiu
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0289
Purpose – Extensive efforts have been conducted on the elimination of position sensors in servomotor control. The purpose of this paper is to aim at estimating the servomotor speed without using position sensors and the knowledge of its parameters by artificial neural networks (ANNs). Design/methodology/approach – A neural speed observer based on the Elman neural network (NN) structure takes only motor voltages and currents as inputs. Findings – After offline NNs training, the observer is incorporated into a DSP-based drive and sensorless control is achieved. Research limitations/implications – Future work will consider to reduce the computation time for NNs training and to adaptively tune parameters on line. Practical implications – The experimental results of the proposed method are presented to show the effectiveness. Originality/value – This paper achieves sensorless servomotor control by ANNs which are seldom studied.
FPGA-based hardware implementation of arctangent and arccosine functions for the inverse kinematics of robot manipulatorKung, Ying-Shieh ; Wu, Ming-Kuang ; Linh Bui Thi and, Hai ; Jung, Tz-Han ; Lee, Feng-Chi ; Chen, Wen-Chuan
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0290
Purpose – The inverse kinematics in robot manipulator have to handle the arctangent and arccosine function. However, the two functions are complicated and need much computation time so that it is difficult to be realized in the typical processing system. The purpose of this paper is to solve this problem by using Field Programmable Gate Array (FPGA) to speed up the computation power. Design/methodology/approach – The Taylor series expansion method is firstly applied to transfer arctangent and arccosine function to a polynomial form. And Look-Up Table (LUT) is used to store the parameters of the polynomial form. Then the behavior of the computation algorithm is described by Very high-speed IC Hardware Description Language (VHDL) and a co-simulation using ModelSim and Simulink is applied to evaluate the correctness of the VHDL code. Findings – The computation time of arctangent and arccosine function using by FPGA need only 320 and 420 ns, respectively, and the accuracy is <0.01°. Practical implications – Fast computation in arctangent and arccosine function can speed up the motion response of the real robot system when it needs to perform the inverse kinematics function. Originality/value – This is the first time such to combine the Taylor series method and LUT method in the computation the arctangent and arccosine function as well as to implement it with FPGA.
Implementation of an automated system for microscope pathological section image with RFID managementChen, Pei-Jarn ; Yeng, Chia-Hong ; Lu, Ma-Mi ; Chen, Sheng-Hsien
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0291
Purpose – The purpose of this paper is to establish an automated microscopic imaging database system using a set of Radio Frequency Identification (RFID) management functions to provide a secure storage for hispathology images. Design/methodology/approach – The automated microscopy imaging system is composed mainly of four parts, which include: first, tissue biopsy image acquisition system, second, image processing system, third, RFID system, and fourth, SQL database system. The system has two modes of operation to store and manage hispathology images. First, the hispathology slide undergoes fluorescence staining before acquiring images directly from an external CCD camera connected to the system. Second, the hispathogical slides that have undergone fluorescence staining undergo another microscopic imaging system, and the contents are extracted into a digitized image archive and imported to the system. Also, the system not only acquires images but also performs functions such as displacement correction, image superimposition, and calculation of the total number of fluorescence points. The two methods mentioned above produce the hispathology image files and are tagged using an RFID string index to establish and manage the database system. Findings – The results demonstrated that in the impurities were effectively eliminated in the red fluorescence staining after binarization processing. However, the blue ones remained the same and to solve this problem an adjustable threshold allows users to select the appropriate threshold. Using an additional eigenvalue code to the RFID string provides better encryption mechanism for the patient files and any attempt to tamper the file can easily be detected through the comparison of the eigenvalues. Originality/value – This paper proposes a novel method to implement a more comprehensive, safe, fast, and automated management system for hispathological images using RFID management and image processing techniques. Additional security is provided by including eigenvalues as encryption mechanisms in the Tag string of the RFID.
Fuzzy visual detection for human-robot interactionShieh, Ming-Yuan ; Hsieh, Chung-Yu ; Hsieh, Tsung-Min
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0292
Purpose – The purpose of this paper is to propose a fast object detection algorithm based on structural light analysis, which aims to detect and recognize human gesture and pose and then to conclude the respective commands for human-robot interaction control. Design/methodology/approach – In this paper, the human poses are estimated and analyzed by the proposed scheme, and then the resultant data concluded by the fuzzy decision-making system are used to launch respective robotic motions. The RGB camera and the infrared light module aim to do distance estimation of a body or several bodies. Findings – The modules not only provide image perception but also objective skeleton detection. In which, a laser source in the infrared light module emits invisible infrared light which passes through a filter and is scattered into a semi-random but constant pattern of small dots which is projected onto the environment in front of the sensor. The reflected pattern is then detected by an infrared camera and analyzed for depth estimation. Since the depth of object is a key parameter for pose recognition, one can estimate the distance to each dot and then get depth information by calculation of distance between emitter and receiver. Research limitations/implications – Future work will consider to reduce the computation time for objective estimation and to tune parameters adaptively. Practical implications – The experimental results demonstrate the feasibility of the proposed system. Originality/value – This paper achieves real-time human-robot interaction by visual detection based on structural light analysis.
A study of risk-adjusted stock selection models using genetic algorithmsHuang, Chien-Feng ; Hsieh, Tsung-Nan ; Rong Chang, Bao ; Chang, Chih-Hsiang
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0293
Purpose – Stock selection has long been identified as a challenging task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. The purpose of this paper is to employ the methods from computational intelligence (CI) to solve this problem more effectively. Design/methodology/approach – The authors develop a risk-adjusted strategy to improve upon the previous stock selection models by two main risk measures – downside risk and variation in returns. Moreover, the authors employ the genetic algorithm for optimization of model parameters and selection for input variables simultaneously. Findings – It is found that the proposed risk-adjusted methodology via maximum drawdown significantly outperforms the benchmark and improves the previous model in the performance of stock selection. Research limitations/implications – Future work considers an extensive study for the risk-adjusted model using other risk measures such as Value at Risk, Block Maxima, etc. The authors also intend to use financial data from other countries, if available, in order to assess if the method is generally applicable and robust across different environments. Practical implications – The authors expect this risk-adjusted model to advance the CI research for financial engineering and provide an promising solutions to stock selection in practice. Originality/value – The originality of this work is that maximum drawdown is being successfully incorporated into the CI-based stock selection model in which the model's effectiveness is validated with strong statistical evidence.
Green technology automotive shape design based on neural networks and support vector regressionFan, Kuo-Kuang ; Chiu, Chun-Hui ; Yang, Chih-Chieh
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0294
Purpose – The green technology cars have received much attention due to the air pollution and energy crisis. The purpose of this paper is to increase automotive designers’ understanding of the affective response of consumers about automotive shape design. Consumers’ preference is mainly based on a vehicle's shape features that are traditionally manipulated by designers’ intuitive experience rather than by an effective and systematic analysis. Therefore, when encountering increasing competition in today's automotive market, enhancing car designers’ understanding of consumers’ preferences on the shape features of green technology vehicles to fulfil customers’ demands, has become a common objective for automotive makers. Design/methodology/approach – In this paper, questionnaires were first used to gather consumer evaluations of certain adjectives describing automobile shape. Then, automotive styling features were systematically examined by numerical definition-based shape representations. Finally, models were individually constructed using support vector regression (SAR), which predicted consumer's affective responses, based on the adjectives selected, and which also incorporated the relationship between consumer's affective responses and automotive styling features. Findings – In order to predict and suggest the best automotive shape design, the results of this experiment of SVR can provide a basis for the future development of automobiles, particularly for green vehicle design, and support automotive makers in ensuring that automotive shape design to satisfy consumer needs. Originality/value – SVR is a valuable choice as an evaluation method to be applied in the design field of green vehicles.
Analysis of virtualized cloud server together with shared storage and estimation of consolidation ratio and TCO/ROIRong Chang, Bao ; Tsai, Hsiu-Fen ; Chen, Chi-Ming ; Huang, Chien-Feng
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0295
Purpose – The physical server transition to virtualized infrastructure server have encountered crucial problems such as server consolidation, virtual machine (VM) performance, workload density, total cost of ownership (TCO), and return on investments (ROIs). In order to solve the problems mentioned above, the purpose of this paper is to perform the analysis of virtualized cloud server together with shared storage as well as the estimation of consolidation ratio and TCO/ROI in server virtualization. Design/methodology/approach – This paper introduces five distinct virtualized cloud computing servers (VCCSs), and provides the appropriate assessment to five well-known hypervisors built in VCCSs. The methodology the authors proposed in this paper will gives people an insight into the problem of physical server transition to virtualized infrastructure server. Findings – As a matter of fact, VM performance seems almost to achieve the same level, but the estimation of VM density and TCO/ROI are totally different among hypervisors. As a result, the authors have the recommendation to choose the hypervisor ESX server if you need a scheme with higher ROI and lower TCO. Alternatively, Proxmox VE would be the second choice if you like to save the initial investment at first and own a pretty well management interface at console. Research limitations/implications – In the performance analysis, instead of ESX 5.0, the authors adopted ESXi 5.0 that is free software, its function is limited, and does not have the full functionality of ESX server, such as: distributed resource scheduling, high availability, consolidated backup, fault tolerance, and disaster recovery. Moreover, this paper do not discuss the security problem on VCCS which is related to access control and cryptograph in VMs to be explored in the further work. Practical implications – In the process of virtualizing the network, ESX/ESXi has restrictions on the brand of the physical network card, only certain network cards can be detected by the VM. For instance: Intel and Broadcom network cards. The newer versions of ESXi 5.0.0 and above now support parts of Realtek series (Realtek 8186, Realtek 8169, and Realtek 8111E). Originality/value – How to precisely assess the hypervisor for server/desktop virtualization is also of hard question needed to deal with it crisply before deploying new IT with VCCS on site. The authors have utilized the VMware calculator and developed an approach to server/desktop consolidation, virtualization performance, VM density, TCO, and ROIs. As a result, in this paper the authors conducted a comprehensive approach to analyze five well-known hypervisors and will give the recommendation for IT manager to choose a right solution for server virtualization.
Optimal high-rigidity structure design for CNC machine tools using CAE techniqueWang, Kun-Chieh
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0296
Purpose – For machine tools, the machining performance is mainly determined by the rigidity of the machine structure. How to design a machine tool with high rigidity is always a challenge issue. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the Computer Aided Engineering (CAE) technique is used to analyze the structure rigidity of a Computer-Numerical Control (CNC) turn-mill machining center. The considered structure parameters include static rigidity and vibration mode. Through the integral analyses of these two structure parameters in conjunction with the practical design experiences, an optimal structure is obtained. Findings – Comparisons between the original prototype and the suggested new design structure via CAE technique under the guide of these two stiffness parameters show a great improvement on the maximal deformation of the machine structure under the action of cutting forces. Originality/value – Through the proposed integrative examination of two structural parameters and the CAE technique, together with design experiences, an optimal CNC machine structure can be obtained.
A classification approach based on variable precision rough sets and cluster validity index functionLin, Hongkang
2014 Engineering Computations
doi: 10.1108/EC-11-2012-0297
Purpose – The clustering/classification method proposed in this study, designated as the PFV-index method, provides the means to solve the following problems for a data set characterized by imprecision and uncertainty: first, discretizing the continuous values of all the individual attributes within a data set; second, evaluating the optimality of the discretization results; third, determining the optimal number of clusters per attribute; and fourth, improving the classification accuracy (CA) of data sets characterized by uncertainty. The paper aims to discuss these issues. Design/methodology/approach – The proposed method for the solution of the clustering/classifying problem, designated as PFV-index method, combines a particle swarm optimization algorithm, fuzzy C-means method, variable precision rough sets theory, and a new cluster validity index function. Findings – This method could cluster the values of the individual attributes within the data set and achieves both the optimal number of clusters and the optimal CA. Originality/value – The validity of the proposed approach is investigated by comparing the classification results obtained for UCI data sets with those obtained by supervised classification BPNN, decision-tree methods.
The visual tracking system using a stereo vision robotYeh, Long-Jyi ; Han Lee, Tsung ; Hsu, Kuei-Shu
2014 Engineering Computations
doi: 10.1108/EC-12-2012-0308
Purpose – The purpose of this paper is to use vision stereo to simultaneously acquire image pairs under a normal environment. Then the methods of moving edges detection and moving target shifting are applied to reduce noise error in order to position a target efficiently. The target is then double confirmed via image merge and alignment. After positioning, the visual difference between the target and the image created by the stereo vision system is measured for alignment. Finally, the image depth of the target is calculated followed by real-time target tracking. Design/methodology/approach – This study mainly applies Sobel image principle. In addition, moving edges detection and moving target shifting are also used to work with system multi-threading for improving image identification efficiency. Findings – The results of the experiment suggest that real-time image tracking and positioning under a pre-set environment can be effectively improved. On the other hand, tracking and positioning are slightly affected under a normal environment. Errors of distance measurements occur because there is more noise existing. Research limitations/implications – This study mainly determines the movements and positioning of an object or a target via image. However, the stability of moving edges detection executed by the stereo vision system can be affected if the light sources in an environment are too strong or extreme. Practical implications – So far the method of tracking and positioning a moving object has been applied to surveillance systems or the application which requires measuring and positioning under a normal environment. The method proposed by this study can also be used to construct a 3D environment. Originality/value – The method proposed by this study can also be used to construct a 3D environment or tracking moving object to measure the distance.