Computer Engineering Commons^™

การบูรณาการระบบความมั่นคงสารสนเทศตามมาตรฐานไอเอสโอ/ไออีซี 27001 กับการจัดการโครงการซอฟต์แวร์, วชิรวิทย์ เซียวสกุล

Chulalongkorn University Theses and Dissertations (Chula ETD)

การบูรณาการระบบการจัดการความมั่นคงสารสนเทศและการจัดการโครงการซอฟต์แวร์มีความสําคัญอย่างยิ่ง เนื่องจากข้อมูลที่เกิดขึ้นในระบบสารสนเทศน้ันมีความละเอียดอ่อน เช่น ข้อมูลทางการเงิน หรือ ข้อมูลส่วนบุคคล เป็นต้น ซึ่งหากข้อมูลสารสนเทศดังกล่าวถูกเข้าถึงโดยไม่ได้รับอนุญาตจากเจ้าของข้อมูล หรือถูกนําไปใช้ในทางที่ไม่ถูกต้อง จะส่งผลให้เกิดความเสียหายต่อเจ้าของข้อมูล รวมถึงองค์กรที่ให้บริการระบบสารสนเทศดังกล่าว ดังนั้นระบบการจัดการความเสี่ยงด้านความมั่นคงสารสนเทศจึงจําเป็นต้องบูรณาการเข้ากับการจัดการโครงการในทุกขั้นตอนของวัฏจักรการพัฒนาซอฟต์แวร์ ปัจจุบันโครงการพัฒนาซอฟต์แวร์มักจะเกิดในสภาพแวดล้อมที่มีการเปลี่ยนแปลงอย่างรวดเร็ว การประยุกต์ใช้แนวปฏิบัติแบบสกรัมจะช่วยเข้ามาตอบโจทย์ความต้องการในการพัฒนาซอฟต์แวร์ที่มีการเปลี่ยนแปลงอย่างรวดเร็วนี้ นอกจากนี้ยังมีความท้าทายในการบูรณาการระบบการจัดการความมั่นคงสารสนเทศกับการจัดการโครงการซอฟต์แวร์แบบสกรัมเฟรมเวิร์กคือ จากการรักษาความสมดุลระหว่างมาตรการความมั่นคงสารสนเทศที่มีความเข้มงวดกับระเบียบวิธีแบบสกรัมที่เน้นความรวดเร็วและคล่องตัว โครงงานนี้ผู้จัดทําโครงงานได้นําเสนอการบูรณาการระบบการจัดการความมั่นคงสารสนเทศกับการจัดโครงการซอฟต์แวร์แบบสกรัม โดยอ้างอิงจากมาตรฐานสากล ความต้องการของระบบจัดการความมั่นคงสารสนเทศ (ISO/IEC 27001:2022) มาตรการควบคุมความมั่นคงสารสนเทศ (ISO/IEC 27002:2022) และ การจัดการความเสี่ยง (ISO/IEC 31000:2018) นอกจากนี้ผู้จัดทําโครงงานได้อ้างอิงองค์ความรู้ด้านการจัดการโครงการซอฟต์แวร์แบบสกรัม จากการจัดการโครงการของสถาบันการจัดการโครงการ (Project Management Institute) และคู่มือสกรัมจากองค์กรแนวทางปฏิบัติสกรัม (Scrum Guides Organization) โดยกระบวนการที่ผ่านการบูรณาการแบ่งออกเป็น 6 ขั้นตอน ซึ่งได้ออกแบบรายละเอียดกิจกรรมและบทบาทของผู้เกี่ยวข้องที่เกิดขึ้นในแต่ละกิจกรรม เพื่อให้การจัดการโครงการซอฟต์แวร์มีความมั่นคงสารสนเทศอย่างทนทาน พร้อมทั้งพัฒนาเครื่องมือต้นแบบสำหรับการจัดการความมั่นคงสารสนเทศของโครงการซอฟต์แวร์แบบสกรัม ซึ่งช่วยสนับสนุนให้การจัดการโครงการมีประสิทธิภาพมากยิ่งขึ้น

Ssvep-Bci In Augmented Reality In Realistic Environment, Bhuwit Chaiyarit

Chulalongkorn University Theses and Dissertations (Chula ETD)

This study investigates Augmented Reality-based Steady-State Visual Evoked Potentials (AR-SSVEP) in Brain-Computer Interface (BCI) systems, utilizing Extended Filter Bank Canonical Correlation Analysis (Extended-FBCCA) for SSVEP recognition optimization across various frequencies in various scenarios. The 10 Hz stimulus consistently achieves the highest recognition accuracy. Frequency-specific variations at 12 Hz, 13 Hz, and 15 Hz highlight nuanced SSVEP responses. White stimuli yield PC-SSVEP recognition ranging from 68.32% to 81.98%, slightly outperforming AR-SSVEP-Controlled (63.59% to 73.23%). Red stimuli impact AR-SSVEP-Controlled less than PC-SSVEP, with recognition ranges of 68.65% to 76.30% and 67.97% to 76.72%, respectively. Comparisons show a general slight outperformance of PC-SSVEP, …

Efficient Gpu Implementation Of Automatic Differentiation For Computational Fluid Dynamics, Mohammad Zubair, Desh Ranjan, Aaron Walden, Gabriel Nastac, Eric Nielsen, Boris Diskin, Marc Paterno, Samuel Jung, Joshua Hoke Davis

Computer Science Faculty Publications

Many scientific and engineering applications require repeated calculations of derivatives of output functions with respect to input parameters. Automatic Differentiation (AD) is a method that automates derivative calculations and can significantly speed up code development. In Computational Fluid Dynamics (CFD), derivatives of flux functions with respect to state variables (Jacobian) are needed for efficient solutions of the nonlinear governing equations. AD of flux functions on graphics processing units (GPUs) is challenging as flux computations involve many intermediate variables that create high register pressure and require significant memory traffic because of the need to store the derivatives. This paper presents a …

A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong

Computer Science Faculty Publications

Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. However, most previous methods treat word embeddings as isolated entities and fail to jointly consider both the intra-space and inter-space topological relations between words. This limitation makes it challenging to align words from embedding spaces with distinct topological structures, especially when the assumption of isomorphism may not hold. To this end, we propose a novel approach called the Structure-Aware Generative Adversarial Network (SA-GAN) model to explicitly capture multiple topological structure information to achieve accurate …

Alternatives To Reducing Aviation Fuel-Burn With Technology: Fully Electric Autonomous Taxibot, Denzil Neo

Student Works

Aircraft taxiing operations in the aerodrome were identified to consume the most jet fuel apart from the cruise phase of the flight. This was also well supported by various research associating taxi operations at large, congested airports, with high jet fuel consumption, high carbon emissions, and noise pollution. Existing literature recognised the potential to address the environmental issues of aerodrome taxi operations by operating External or Onboard Aircraft Ground Propulsion Systems (AGPS). Designed to power aircraft with sources other than their main engines, external Aircraft Ground Power Systems (AGPS) have shown the potential to significantly cut jet fuel consumption and …

Exploiting The Advantages And Overcoming The Challenges Of The Cable In A Tethered Drone System, Rogerio Rodrigues Lima

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation proposes solutions for motion planning, localization, and landing of tethered drones using only tether variables. A tether-based multi-model localization framework for tethered drones is proposed. This framework comprises three independent localization strategies based on a different model. The first strategy uses simple trigonometric relations assuming that the tether is taut; the second method relies on a set of catenary equations for the slack tether case; the third estimator is a neural network-based predictor that can cover different tether shapes. Multi-layer perceptron networks previously trained with a dataset comprised of the tether variables (i.e., length, tether angles on the …

Co-Design Of An Interactive Wellness Park: Exploring Design Requirements For A Multimodal Outdoor Physical Web Installation With Older Adults, Fatima Badmos

Academic Posters Collection

The global demographic landscape is experiencing a notable shift, characterised by a growing proportion of adults over 60. According to projections, the proportion of individuals aged 60 and above is expected to reach one-sixth of the global population by 2030. Furthermore, by 2050, this demographic is projected to exceed a staggering two billion people. Amidst this shift, there is an urgent need to develop interactive and innovative solutions to address older adults' unique challenges, particularly in outdoor physical activity.

A co-design methodology involving older adults’ participation from the idea generation to the application development process will be adopted to address …

To The Moon: Strategic Competition In The Cislunar Region, Shawn M. Willis

Faculty Publications

China’s advancing space capabilities, particularly in the cislunar region, call for increased cislunar space domain awareness on the part of the United States. US military and civilian decisionmakers must take into account the full scope of China’s cislunar plans and capabilities as the military builds space strategies and future force designs. The United States must also increase near-term investments that support more robust cislunar space domain awareness.

An Adaptive Image Restoration Algorithm Based On Hybrid Total Variation Regularization, Cong Thang Pham, Thi Thu Thao Tran, Hung Vi Dang, Hoai Phuong Dang

Turkish Journal of Electrical Engineering and Computer Sciences

In imaging systems, the mixed Poisson-Gaussian noise (MPGN) model can accurately describe the noise present. Total variation (TV) regularization-based methods have been widely utilized for Poisson-Gaussian removal with edge-preserving. However, TV regularization sometimes causes staircase artifacts with piecewise constants. To overcome this issue, we propose a new model in which the regularization term is represented by a combination of total variation and high-order total variation. We study the existence and uniqueness of the minimizer for the considered model. Numerically, the minimization problem can be efficiently solved by the alternating minimization method. Furthermore, we give rigorous convergence analyses of our algorithm. …

Deep Learning-Based Classification Of Chaotic Systems Over Phase Portraits, Sezgi̇n Kaçar, Süleyman Uzun, Burak Aricioğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This study performed a deep learning-based classification of chaotic systems over their phase portraits. To the best of the authors' knowledge, such classification studies over phase portraits have not been conducted in the literature. To that end, a dataset consisting of the phase portraits of the most known two chaotic systems, namely Lorenz and Chen, is generated for different values of the parameters, initial conditions, step size, and time length. Then, a classification with high accuracy is carried out employing transfer learning methods. The transfer learning methods used in the study are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet, and …

A Type-2 Fuzzy Rule-Based Model For Diagnosis Of Covid-19, İhsan Şahi̇n, Erhan Akdoğan, Mehmet Emi̇n Aktan

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical …

An Effective Hilbert-Huang Transform-Based Approach For Dynamic Eccentricity Fault Diagnosis In Double-Rotor Double-Sided Stator Structure Axial Flux Permanent Magnet Generator Under Various Load And Speed Conditions, Makan Torabi, Yousef Alinejad Beromi

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity fault in double-sided axial flux permanent magnet generator is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic, especially at the initial stages of the fault occurrence. In addition, one of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned difficulty and problems, this paper adopts a novelty detection algorithm based on Hilbert-Huang transform (HHT) which is a time-frequency signal analysis approach based on empirical mode decomposition and the Hilbert transform. It is …

Transmission Network Planning For Realistic Egyptian Systems Via Encircling Prey Based Algorithms, Abdullah M. Shaheen, Ragab Elsehiemy, Mohammed Kharrich, Salah Kamel

Turkish Journal of Electrical Engineering and Computer Sciences

Transmission network planning problem (TNPP) is one of the pertinent issues of the planning activities in power systems. It aims to optimally pick out the routs, types, and number of the new installed lines to confront the expected future loading conditions. In this line, this study proposes a new economic model to the TNPP. The aim of the model is to find the optimal transmission routes at least investment and operating costs. Three recent algorithms called grey wolf optimization algorithm (GWOA), spotted hyena optimization algorithm (SHOA) and whale optimization algorithm (WOA) are developed to solve the TNPP. The concept of …

Early Diagnosis Of Pancreatic Cancer By Machine Learning Methods Using Urine Biomarker Combinations, İrem Acer, Firat Orhan Bulucu, Semra İçer, Fatma Lati̇foğlu

Turkish Journal of Electrical Engineering and Computer Sciences

The most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancers. The five-year survival rate for PDAC due to late diagnosis is 9%. Early diagnosed PDAC patients survive longer than patients diagnosed at a more advanced stage. Biomarkers can play an essential role in the early detection of PDAC to assist the health professional. Machine learning and deep learning methods are used with biomarkers obtained in recent studies for diagnostic purposes. In order to increase the survival rates of PDAC patients, early diagnosis of the disease with a noninvasive test …

Binary Text Classification Using Genetic Programming With Crossover-Based Oversampling For Imbalanced Datasets, Mona Aljero, Nazi̇fe Di̇mi̇li̇ler

Turkish Journal of Electrical Engineering and Computer Sciences

It is well known that classifiers trained using imbalanced datasets usually have a bias toward the majority class. In this context, classification models can present a high classification performance overall and for the majority class, even when the performance for the minority class is significantly lower. This paper presents a genetic programming (GP) model with a crossover-based oversampling technique for oversampling the imbalanced dataset for binary text classification. The aim of this study is to apply an oversampling technique to solve the imbalanced issue and improve the performance of the GP model that employed the proposed technique. The proposed technique …

Lvq Treatment For Zero-Shot Learning, Firat İsmai̇loğlu

Turkish Journal of Electrical Engineering and Computer Sciences

In image classification, there are no labeled training instances for some classes, which are therefore called unseen classes or test classes. To classify these classes, zero-shot learning (ZSL) was developed, which typically attempts to learn a mapping from the (visual) feature space to the semantic space in which the classes are represented by a list of semantically meaningful attributes. However, the fact that this mapping is learned without using instances of the test classes affects the performance of ZSL, which is known as the domain shift problem. In this study, we propose to apply the learning vector quantization (LVQ) algorithm …

The Metaverse: A Virtual World In The Palm Of Your Hand, Ziad Doughan, Hadi Al Mubasher, Mustafa El Bizri, Ali Haidar

BAU Journal - Science and Technology

This paper explores the actual and future impact of the Metaverse as a virtual space. Thus, it focuses the probe on the technical challenges that face this everlasting emerging technology. Today, the Metaverse presents a digital environment to build collective architecture and historical heritage in a virtual space. In this digital world, the modeling and design methodology is based on individual archetypes that can puzzle new elements. Currently, traditional methods require change and adaptation in both the education and work market, especially due to the remote-work integration in the last few years. For example, many components are required to build …

Examining Early Elementary Computer Science Identity Repertoires Within A Curriculum: Implications For Epistemologically Pluralistic Identities, Eleanor Richard, Shakhnoza Kayumova

Journal of Computer Science Integration

As computer science (CS) enters an increasing number of elementary classrooms, researchers must investigate the representations of what kinds of people are presented as doing computer science within CS curricula. In this paper, we explore a widely used, freely accessible, web-based, early elementary CS curriculum to examine the kinds of identity repertoires (behaviors, actions, skills, and socioemotional norms) that are promoted as representative of being/becoming a CS person. More specifically, we draw on identity studies and employ critical discourse analysis to examine how the kinds of norms and repertoires of CS practice made available in the curricular materials might construct …

Hybrid Artificial Bee Colony And Improved Simulatedannealing For The Capacitated Vehicle Routing Problem, Farhanna Mar'i, Hafidz Ubaidillah, Wayan Firdaus Mahmudy, Ahmad Afif Supianto

Knowledge Engineering and Data Science

Capacitated Vehicle Routing Problem (CVRP) is a type of NP-Hard combinatorial problem that requires a high computational process. In the case of CVRP, there is an additional constraint in the form of a capacity limit owned by the vehicle, so the complexity of the problem from CVRP is to find the optimum route pattern for minimizing travel costs which are also adjusted to customer demand and vehicle capacity for distribution. One method of solving CVRP can be done by implementing a meta-heuristic algorithm. In this research, two meta-heuristic algorithms have been hybridized: Artificial Bee Colony (ABC) with Improved Simulated Annealing …

An Accurate Real-Time Method For Face Mask Detectionusing Cnn And Svm, Shili Hechmi

Knowledge Engineering and Data Science

Infectious respiratory diseases, including COVID-19, pose a significant challenge to humanity and a potential threat to life due to their severity and rapid spread. Using a surgical mask is among the most significant safety precautions that can help keep this sort of pandemic from spreading, and manual monitoring of large crowds in public places for face masks is problematic. In this research, we suggest a real-time approach for face mask detection. First, we use a multi-scale deep neural network to extract features. As a result, the attributes are better suited for training the detection system. We employ SVM post-processing in …

Indonesian Language Term Extraction Using Multi-Task Neural Network, Joan Santoso, Esther Irawati Setiawan, Fransiskus Xaverius Ferdinandus, Gunawan Gunawan, Leonel Hernandez Collantes

Knowledge Engineering and Data Science

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two …

Adaptive Neuro-Fuzzy Inference System For Waste Prediction, Haviluddin Haviluddin, Herman Santoso Pakpahan, Novianti Puspitasari, Gubtha Mahendra Putra, Rima Yustika Hasnida, Rayner Alfred

Knowledge Engineering and Data Science

The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as a decrease in public health. Therefore, predicting the volume of landfills with a high degree of accuracy is needed as a reference for government agencies and the community in making future policies. This study aims to analyze the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The prediction results' accuracy level is measured by the value of the Mean Absolute Percentage Error (MAPE). The final results of this study were obtained from the best MAPE test results. The best …

Associated Patterns In Open-Ended Concept Maps Within E-Learning, Didik Dwi Prasetya, Tsukasa Hirasama

Knowledge Engineering and Data Science

A concept map is a diagram that visualizes the structure of individual cognitive knowledge. An approach to creating a concept map structure that allows users to contribute concepts and linkages that express their understanding freely is known as an "open-ended concept map." It has been demonstrated that an open-ended concept map accurately depicts student knowledge structures and reveals student differences. However, manually analyzing an open-ended map is difficult, time-consuming, and includes many propositions, especially in a big classroom. Educational data mining could be used to further process and analyze a collection of concept maps. However, many works attempted to employ …

Overview Of Research And Application On Autonomous Vehicle Oriented Perception System Simulation, Ruoxuan Wang, Jianping Wu, Hui Xu

Journal of System Simulation

Abstract: Following the rapid progress of science and technology, vehicles with autonomous driving or auxiliary driving function enter into vehicle market. However, in the past decade, traffic accidents still occurred frequently, and the safety of these functions become the focus. Simulation technology provides a good platform to test the perception system of autonomous vehicle. Focus on the sensor simulation modeling of autonomous vehicle perception system, from the perspective of single sensor simulation, multi-sensor simulation and classic simulation platform including millimeter wave radar, lidar and camera, the existing research are reviewed, and the shortcomings and development trends of simulation modeling of …

Anomaly Detection Method Of Electrical Power Consumption Based On Deep Autoencoder, Ningke Sun, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the nonlinear and non-stationary characteristics of electrical power consumption data, an abnormal electrical power consumption detection model based on deep autoencoder is proposed. Gated recurrent unit (GRU) network of the deep learning is combined with autoencoder structure, and the encoder and decoder parts of traditional autoencoder are realized by gated recurrent unit network, which gives full play to the data feature extraction capability of gated recurrent unit and the data reconstruction function of autoencoder structure. Based on the reconstruction error between original data and reconstructed data, abnormal data points of the electrical power consumption are detected. By …

Short-Term Prediction Method Of Wind Power Based On Blp-Alo-Svm, Yefeng Jiao, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: To effectively predict the short-term wind power and its fluctuation range, a prediction method based on hybrid algorithm-optimized support vector machine is proposed. Exploratory data analysis is used to preprocess the original wind speed data to improve the data quality. Chaotic map, Levy flight strategy and particle swarm optimization are used to improve the ant lion algorithm. The support vector machine model optimized by hybrid algorithm is used to predict the wind power. The experimental results show that, compared with the new wind power prediction model, the prediction error of the output results of the method is lower, and …

Long-Term Resilience Simulation On Low-Carbon Urban Grid Based On Evolutionary Game, Zhengda Cui, Weiqiang Yao, Qin Xu, Chen Fang, Ying Chen

Journal of System Simulation

Abstract: Because of the more frequent extreme disasters, the resilience of urban grid becomes more important. In the background of carbon neutralization policy, the decarbonization transition of urban grid also affects the development of grid resilience. An evolutionary game is used to simulate the resilience evolution of low-carbon urban grid and the evolution model is constructed. The decision to install photovoltaic and energy storage system for residents and to upgrade the grid for resilience is considered in the model and the stability conditions of equilibriums of the evolutionary game are analyzed. The resilience evolution simulation model considering disaster stochasticity is …

Simulation-Driven Based Utility Evaluation And Recommendation Of Expressway Proactive Speed Limit, Geqi Qi, Sijin Liu, Yikang He, Meng Wang, Ailing Huang

Journal of System Simulation

Abstract: Outside the specific punishment area, the traditional roadside passive speed limit mode lacks traffic management, and thus which indirectly leads to the inconsistency or even sudden change of vehicle behaviors in time and space, thereby affects the traffic efficiency and safety. Focusing on the proactive speed limit mode at vehicle side, a utility evaluation and recommendation method is proposed, which carries out the multi-scenario traffic simulation for varied proactive and passive speed limit considering road line types, traffic flow and vehicle type proportion. From the two perspectives of safety and efficiency, the utility evaluation indicators and weights are extracted …

Simulation On Flood Disaster In Urban Building Complex System Based On Lbm, Shen Zhang, Zewang Yang, Yifan Wang, Liang Sun, Ming Cheng, Fankai Meng, Ting Li

Journal of System Simulation

Abstract: Because of the extreme climate change, the potential flood disaster risk in the southeast coastal areas of China can not be ignored. Based on lattice Boltzmann computational fluid dynamics method, a three-dimensional simulation study of waterlogging process in tsunami impact scenario is carried out for a coastal city building complex system, and the reliability and accuracy of the numerical simulation method for the flood impact test of an ideal building complex are verified. The results show that the buildings along rivers and coastlines have obvious cloaking effect, while the buildings inside the city are less affected by floods. The …

Research On Modeling And Simulation Technology Of Microwave Radar High Precision Tracking Loop, Jiaji Lou, Jing Ma, Xiaowei Li, Yue Zhao, Youbin Song

Journal of System Simulation

Abstract: Aiming at the key problem of high-precision tracking loop design of a measurement radar for the weak signal (low carrier to noise ratio signal) in dynamic environment, the design and optimization methods of carrier tracking loop and code tracking loop are focused on. A signal structure with a single carrier as a pilot is proposed, and the loop structure of the frequency-locked loop and the phase-locked loop working together is studied. The modeling and simulation on the two structures show that for the week signal tracking in a dynamic environment, the loop combination structure of the frequency-serial auxiliary …