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2017 7th IEEE International Conference on System Engineering and Technology (ICSET)最新文献

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Interictal epileptic discharge EEG detection based on wavelet and multiresolution analysis 基于小波和多分辨率分析的癫痫放电间期脑电图检测
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123435
M. S. Fathillah, R. Jaafar, K. Chellappan, R. Remli, W. Zainal
Epileptologists use interictal epileptic discharge (lED) as a marker for epilepsy. The present conventional method to distinguish normal and I ED by an epileptologist's visual screening is tedious and operator dependent. The focus of this paper is to distinguish normal and IED in clinically recorded electroencephalogram (EEG) using discrete wavelet transform. Wavelet multiresolution analysis has been adopted in this study looking into wavelet energy, wavelet entropy and amplitude dispersion in every sub-band. The extracted features were classified using support vector machine (SVM). EEG data were obtained from both online database and Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) Neurology database. The ability of the proposed algorithm in detecting the presence of IED is 96.5% of accuracy, 100% of sensitivity and 95.5% of specificity. The algorithm has good potential to be used in clinical practice for IED detection with validation against the present clinical detection method.
癫痫学家使用间歇性癫痫放电(lED)作为癫痫的标志。目前通过癫痫医生的视觉筛查来区分正常ED和I ED的常规方法繁琐且依赖于操作人员。本文的重点是利用离散小波变换对临床记录的脑电图进行区分。本研究采用小波多分辨分析方法,研究小波能量、小波熵和各子带的幅值色散。利用支持向量机(SVM)对提取的特征进行分类。脑电图数据来自在线数据库和马来西亚国立大学神经病学数据库。该算法检测IED的准确率为96.5%,灵敏度为100%,特异度为95.5%。通过对现有临床检测方法的验证,该算法具有良好的临床应用潜力。
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引用次数: 1
The characteristics of collaborative portfolio assessment learning system as a tools in school based assessment environment 协同档案袋评估学习系统在校本评估环境下的特点
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123410
Suhaizal Hashim, Affero Ismail, A. Masek
School Based Assessment (SBA) has been in place since 2012 in Malaysia. Amongst the main purpose of it was to improve students' learning and assessment system. However, school still fails to explain and implement a broader assessment process that can improve teaching and learning and still using manual filing systems to manage and process the students result. Therefore, Collaborative Portfolio Assessment (CPA) learning system that use to scaffold students' knowledge was purposed as a solution. This paper presents the main characteristics of CPA learning system that was embedded with an interactive learning approach using group based activities to encourage students' active participation in their learning. The CPA learning system is a learning environment consuming the principle elements from knowledge management model, Computer Supported Collaborative Learning (CSCL) approaches and eportfolio as a learning documentation model. It also allows students to learn together through computer-mediated networks. CSCL learning strategies has been implemented with an eportfolio as a learning documentation model that expect to foster knowledge construction process by involving students to learn together through computer mediated networks. These process plans to help and manage the implementation of SBA in Malaysian school systematically. Assessment process conducted on this CPA learning system is hope to benefits teachers with emphasizing on assessment in the learning aspect and assisting to overcome issues of documentation filing, missing data, human negligence and to ensure that latest updates and information's about assessment is spread accordingly.
校本评估(SBA)自2012年起在马来西亚实施。其主要目的之一是改善学生的学习和评估系统。然而,学校仍然没有解释和实施一个更广泛的评估过程,可以改善教学和学习,仍然使用手动归档系统来管理和处理学生的成绩。因此,利用协作档案袋评估(CPA)学习系统来支撑学生的知识是一种解决方案。本文介绍了注册会计师学习系统的主要特点,该学习系统嵌入了一种交互式学习方法,使用基于小组的活动来鼓励学生积极参与学习。注册会计师学习系统是一个学习环境,它消耗了知识管理模型、计算机支持的协同学习(CSCL)方法和作为学习文档模型的电子投资组合的主要元素。它还允许学生通过计算机媒介网络共同学习。CSCL的学习策略以电子档案库作为学习文档模型来实施,期望通过计算机媒介网络让学生共同学习来促进知识建构过程。这些过程计划系统地帮助和管理SBA在马来西亚学校的实施。在本注册会计师学习系统上进行的评估过程,希望能使教师受益,强调学习方面的评估,帮助克服文件归档、数据缺失、人为疏忽等问题,确保最新的评估更新和信息及时传播。
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引用次数: 3
Implementation of first order model reference adaptive control (MRAC) on regulating temperature of essential oil extraction process 一阶模型参考自适应控制(MRAC)在精油提取过程温度调节中的实现
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123419
Muhammad Hafiz Allias, Z. Muhammad, Z. Yusoff, M. Rahiman
This paper presents the performance of proportional-integral-derivative (PID) controller and Model Reference Adaptive Controller (MRAC) on regulating essential oil extraction process. The quality of essential oil will reduce if the extraction process expose to overheating of temperature. AutoRegressive with Exogenous Input (ARX) model represent as heating process plant. Heating process is important thing in the essential oil extraction and became challenging for industrial to control the temperature at the desired temperature. Besides, quality of essential oil will reduce if the extraction process expose to overheating of temperature. Generally, conventional PID controller that commonly used in control system is not provided a desired output performance. Implementation of MRAC by using Lyapunov approach in the system gives better response than conventional PID controller. Model reference of the system was developed based on first order plus dead time (FOPDT). In addition, ±0.1 is the best selection for adaptation gain of MRAC system. Result shows that MRAC controller was able to minimize its overshoot and robust in performance better than conventional PID controller. Conventional PID controller provided faster time taken in rise time, 1760 sec to initially reach the set point compared to MRAC, 2500 sec but MRAC takes in short time to settle the response, 4460 sec compared to PID, 7220 sec. The comparison of output response between both controllers was obtained by using transient analysis and performance indices in MATLAB/Simulink.
研究了比例-积分-导数(PID)控制器和模型参考自适应控制器(MRAC)对精油提取过程的调节性能。如果萃取过程暴露在过热的温度下,精油的质量会降低。带有外源输入的自回归(ARX)模型表示为加热过程装置。加热过程是精油提取过程中的一个重要环节,如何将加热过程控制在理想的温度下成为工业上的一个挑战。此外,如果萃取过程暴露在温度过高的情况下,精油的质量会降低。一般来说,控制系统中常用的传统PID控制器不能提供理想的输出性能。采用李雅普诺夫方法实现的MRAC比传统的PID控制器具有更好的响应。基于一阶加死区时间(FOPDT)建立了系统的模型参考。此外,±0.1是MRAC系统自适应增益的最佳选择。结果表明,与传统的PID控制器相比,MRAC控制器具有较好的鲁棒性和超调性。传统PID控制器的上升时间更快,初始达到设定值比MRAC快1760秒,比MRAC快2500秒,而MRAC的稳定响应时间较短,比PID快4460秒,比PID快7220秒。通过MATLAB/Simulink中的瞬态分析和性能指标,比较了两种控制器的输出响应。
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引用次数: 1
Real time wireless accident tracker using mobile phone 使用手机的实时无线事故追踪器
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123430
N. Ya'acob, Ainnur Eiza Azhar, A. L. Yusof, S. S. Sarnin, D. Ali, A. Anuar
Vehicle accidents are one of the most leading causes of fatality. The time between an accident occurrence and the emergency medical personnel are dispatched to the location of accident is the critical factor in the survival rate after an accident. By minimizing the gap time, mortality rate can be reduced. One approach to reduce the delay time is to use the Real Time Wireless Accident Tracker Using Mobile Phone, a system which automatically senses the occurrence of an accident of a vehicle and immediately notify central emergency dispatch server of the location of accident using Global Positioning System (GPS). The main purpose of this research is as an early accident detection. This system uses PIC 16F microcontroller, piezoelectric sensors, GPS and Global System for Mobile (GSM) modules to detect traffic accidents. When an accident occurs, the piezoelectric sensor detects and measures the severity of the force impacted on the vehicle and sends out a help message to central emergency server. Upon acknowledgement from the server, the system sends out another message containing GPS coordinate of the location.
交通事故是造成死亡的最主要原因之一。从事故发生到紧急医疗人员赶到事故现场的时间是影响事故后存活率的关键因素。通过缩短间隔时间,可以降低死亡率。减少延迟时间的一种方法是使用使用手机的实时无线事故跟踪系统,该系统可以自动感知车辆发生事故,并通过全球定位系统(GPS)立即将事故位置通知中央应急调度服务器。本研究的主要目的是作为事故的早期检测。该系统采用pic16f单片机、压电传感器、GPS和GSM模块来检测交通事故。当发生事故时,压电传感器检测并测量车辆受到的力的严重程度,并向中央应急服务器发送帮助信息。在服务器确认后,系统将发送另一条包含该位置GPS坐标的消息。
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引用次数: 8
Energy efficient torque control using integrated battery-electric vehicle model 基于集成电池-电动汽车模型的节能转矩控制
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123450
R. Ristiana, A. S. Rohman, A. Purwadi, C. Machbub
This paper validates the advantage of using an integrated electric vehicle-battery model in torque control application. In the integrated electric vehicle-battery model, battery dynamics is explicitly included in the model. A well-known linear quadratic integral (LQI) design is adopted here to have an optimal controller with zero tracking error. The energy consumption is, however, observed through several energy consumption formulations. The performance index of the LQI is set to include input-state multiplication, i.e. representing one usual form of energy formulation. The controller designs are performed using linearized model of the integrated electric vehicle-battery model. By applying the linear LQI control to the non-linear integrated electric vehicle battery model, simulations are then taken. The simulation result shows that torque motor control with the inclusion of input-state multiplication in the LQI's performance index present a more energy efficient control in comparison to LQI design without such inclusion in its performance index. This may lead to a potential use of integrated electric vehicle battery model to have an efficient battery usage in controlling an electric vehicle.
本文验证了电动汽车-电池集成模型在转矩控制应用中的优势。在电动汽车-电池集成模型中,明确地将电池动力学纳入模型。本文采用了著名的线性二次积分(LQI)设计,实现了零跟踪误差的最优控制器。然而,能源消耗是通过几个能源消耗公式来观察的。LQI的性能指标被设置为包括输入状态乘法,即代表一种通常形式的能量公式。采用电动汽车-电池集成模型的线性化模型进行控制器设计。将线性LQI控制应用于非线性集成电动汽车电池模型,进行了仿真。仿真结果表明,在LQI的性能指标中包含输入状态乘法的转矩电机控制比不包含输入状态乘法的LQI设计更节能。这可能会导致集成电动汽车电池模型的潜在使用,以便在控制电动汽车时有效地使用电池。
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引用次数: 7
A minimal convolutional neural network for handwritten digit recognition 手写体数字识别的最小卷积神经网络
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123441
M. Teow
The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network using a minimal model. The proposed minimal convolutional neural network is presented using a layering approach. This approach provides a clear understanding of the main mathematical operations in a convolutional neural network. Hence, it benefits beginners and non-mathematical prolific researchers to understand the operation of a convolutional neural network without having an intimidating experience. A handwritten digit recognition using MNIST handwritten digit dataset is used to experiment the performance of the proposed minimal convolutional neural network.
本文的贡献是在理解卷积神经网络的数学结构和使用最小模型的计算实现方面架起了桥梁。采用分层方法提出了最小卷积神经网络。这种方法提供了对卷积神经网络中主要数学运算的清晰理解。因此,它有利于初学者和非数学多产的研究人员了解卷积神经网络的操作,而不必有令人生畏的经验。利用MNIST手写数字数据集进行手写数字识别,实验了所提出的最小卷积神经网络的性能。
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引用次数: 13
Peripheral pulmonary lesion classification from endobronchial ultrasonography images using weight-sum of upper and lower GLCM feature 利用上下GLCM特征加权和对支气管超声图像进行肺周围病变分类
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123438
Banphatree Khomkham, A. Wattanathum, R. Lipikorn
This paper aims to classify a peripheral pulmonary lesion whether it is malignant or benign by proposing the new method to select a window of interest (WOI) using window slicing and the new feature called the "weight-sum of upper and lower gray level co-occurrence matrix (GLCM)" of an endobronchial ultrasound (EBUS) image. The proposed feature can be used to determine the heterogeneity of pulmonary lesion which is one of the most important characteristics of lung cancer. The proposed feature is used as input into three different feature selection methods and three different classifiers for lesion classification. In order to evaluate the classification, a set of 89 EBUS images were used as a sample set. The classifications were performed three times with three different sets of features that were extracted from sample images using the same classification process. The first set of features consists of only standard features which are mean, contrast, homogeneity, correlation, entropy, and energy. The second set of features consists of the proposed feature, and the last set of features consists of both standard features and the proposed feature. The classification results show that using genetic selection as feature selection method with support vector machine as classifier with only the proposed features as input data gives the highest accuracy rate. The statistical results show that the accuracy, the sensitivity, and the specificity are 84.27%, 82.53%, and 88.46%, respectively.
本文提出了利用窗切片选择兴趣窗(WOI)的新方法和支气管超声(EBUS)图像的“上下灰度共生矩阵(GLCM)权值和”的新特征,旨在对肺周围病变的恶性或良性进行分类。提出的特征可以用来确定肺病变的异质性,这是肺癌最重要的特征之一。将所提出的特征作为三种不同特征选择方法和三种不同分类器的输入,对病变进行分类。为了评价分类效果,以89张EBUS图像作为样本集。使用相同的分类过程从样本图像中提取三组不同的特征,进行三次分类。第一组特征仅由标准特征组成,即平均值、对比度、同质性、相关性、熵和能量。第二组特征由提议的特征组成,最后一组特征由标准特征和提议的特征组成。分类结果表明,采用遗传选择作为特征选择方法,支持向量机作为分类器,仅将提出的特征作为输入数据,准确率最高。统计结果显示,该方法的准确率为84.27%,灵敏度为82.53%,特异性为88.46%。
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引用次数: 2
Quadratic programming implementations of model predictive control on arduino for BLDC speed control 模型预测控制在arduino上的二次规划实现
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123448
A. S. Rohman, Hanif F. Prasetyo
Many physical process systems have performance limitation regardless the input. This limitation usually occurs in the form of input saturation of actuator as the constraint of the system. To overcome this problem, Model Predictive Control (MPC) may be used due to its capability to compute optimal control signal in the presence of input saturation. The optimal control signal is obtained by solving a quadratic programming (QP) problem with variables' constraints at each time instants. QP problem with lower and upper constraint is known to be equivalent to an algebraic loop involving diagonal upper and lower saturation. Hence, QP can be iteratively computed until its solution converges. A slow computation of QP, however, limits its applications for fast systems with simple digital processor. Therefore, in order to be able to be implemented in real-time embedded applications, a fast algorithm of QP solver is in need. In this paper, a comparison study of two QP implementation, i.e. Projected Gauss Seidel (PGS) and so called Algorithm-2, using Arduino Mega 2560 is presented. In this case, MPC with QP solver using both algorithms is implemented to control the speed of lightly loaded BLDC motor. Before implementation, system modeling, controller designing, and simulation are undertaken. The performance of both iterative algorithms are then compared in terms of average computation time in the simulation and implementation. It can be observed from the results that Algorithm-2 gives a bit faster computation than PGS under same conditions.
无论输入是什么,许多物理进程系统都有性能限制。这种限制通常以执行器输入饱和的形式出现,作为系统的约束。为了克服这个问题,模型预测控制(MPC)可以在输入饱和的情况下计算出最优的控制信号。通过求解具有变量约束的二次规划(QP)问题,得到各时刻的最优控制信号。已知具有上下约束的QP问题等价于一个包含对角上下饱和的代数环。因此,QP可以迭代计算,直到其解收敛为止。然而,由于QP的计算速度较慢,限制了它在具有简单数字处理器的快速系统中的应用。因此,为了能够在实时嵌入式应用中实现,需要一种快速的QP求解算法。本文对使用Arduino Mega 2560的两种QP实现,即投影高斯塞德尔(PGS)和所谓的Algorithm-2进行了比较研究。在这种情况下,实现MPC与QP求解器同时使用这两种算法来控制轻载无刷直流电机的速度。在实现之前,进行了系统建模、控制器设计和仿真。然后比较了两种迭代算法在仿真和实现中的平均计算时间。从结果可以看出,在相同条件下,算法-2的计算速度比PGS略快。
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引用次数: 0
Breast ultrasound image segmentation based on neutrosophic set and watershed method for classifying margin characteristics 基于中性粒细胞集和分水岭法的乳腺超声图像分割边缘特征分类
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123418
H. A. Nugroho, Yuli Triyani, M. Rahmawaty, I. Ardiyanto
Breast cancer is the leading cause of death in women worldwide. Ultrasonography (USG) is one of the imaging modalities which is widely used to detect and classify the mass abnormalities of the breast nodule. The use of image processing in the development a computer aided diagnosis (CADx) can assist the radiologists in analysing and interpreting the abnormalities of ultrasound nodules. This paper proposes an approach to classify the characteristics of breast nodule into circumscribed and not circumscribed classes. The proposed approach is implemented on 102 breast nodule images comprising of 57 circumscribed and 45 not circumscribed margins. Seven relevant features are extracted from nodule which is automatically segmented by combination neutrosophic set and watershed methods. The classification process based on multi-layer perceptron (MLP) classifier obtains the sensitivity of 96.49%, NPV of 95.35% and AUC of 0.972. These results indicate that the proposed approach successfully classify the margin characteristics of breast ultrasound nodule.
乳腺癌是全世界妇女死亡的主要原因。超声检查(USG)是目前广泛应用于乳腺结节肿块异常诊断和分类的影像学手段之一。利用图像处理技术开发计算机辅助诊断(CADx),可以帮助放射科医生分析和解释超声结节的异常。本文提出了一种将乳腺结节特征划分为界限型和非界限型的方法。所提出的方法在102个乳房结节图像上实施,其中包括57个边界和45个非边界边缘。从结节中提取7个相关特征,采用中性集和分水岭相结合的方法自动分割结节。基于多层感知器(MLP)分类器的分类过程灵敏度为96.49%,NPV为95.35%,AUC为0.972。结果表明,该方法对乳腺超声结节的边缘特征进行了分类。
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引用次数: 12
Relative humidity sensor based on MWCNTs-doped polymer microfiber 基于mwcnts掺杂聚合物微纤维的相对湿度传感器
Pub Date : 2017-10-01 DOI: 10.1109/ICSENGT.2017.8123440
N. M. Isa, N. Irawati, H. A. Rahman, M. H. M. Yusoff, S. Harun, F. Ahmad
A relative humidity sensor based on Multi Walled Carbon Nanotubes (MWCNTs) doped with polymer Poly(methyl methacryate) was proposed in this paper. The fabrication technique applied in this paper is a simple direct drawing method that was able to produce a uniform waist microfiber with a minimum diameter of 5 μm with a length of 5 mm. This microfiber was tested on a different range of relative humidity from 45% to 80%. Performance of the fabricated sensor were then analyzed, focusing at the sensitivity of undoped and MWCNTs-doped polymer microfiber. Based on the results, MWCNTs-doped polymer microfiber exhibits higher sensitivities and better resolution compared to undoped polymer microfiber, with sensitivity and resolution obtained at 0.3341 dBm/% and 1.649% respectively. Indeed, the sensor exhibited a high reliability as well as repeatability. Therefore, this study provides a good platform for enhancing the concept of doping nanomaterials like MWCNTs into PMMA for a chemical and biosensor application.
提出了一种基于多壁碳纳米管(MWCNTs)掺杂聚合物聚甲基丙烯酸甲酯的相对湿度传感器。本文采用的制作技术是一种简单的直接拉丝法,可以制作出最小直径为5 μm,长度为5 mm的均匀腰型超细纤维。这种超细纤维在相对湿度从45%到80%的不同范围内进行了测试。然后分析了传感器的性能,重点分析了未掺杂和掺杂纳米碳管的聚合物微光纤的灵敏度。结果表明,与未掺杂的聚合物微光纤相比,掺杂mwcnts的聚合物微光纤具有更高的灵敏度和更好的分辨率,灵敏度和分辨率分别为0.3341 dBm/%和1.649%。实际上,该传感器具有很高的可靠性和可重复性。因此,本研究为将MWCNTs等纳米材料掺杂到PMMA中用于化学和生物传感器的应用提供了一个很好的平台。
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引用次数: 1
期刊
2017 7th IEEE International Conference on System Engineering and Technology (ICSET)
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