Computer Sciences Commons^™

Fault Identification On Electrical Transmission Lines Using Artificial Neural Networks, Christopher W. Asbery

Theses and Dissertations--Electrical and Computer Engineering

Transmission lines are designed to transport large amounts of electrical power from the point of generation to the point of consumption. Since transmission lines are built to span over long distances, they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. When faults occur, protection systems installed near the faulted transmission lines will isolate these faults from the …

On The Automorphisms And Isomorphisms Of Mds Matrices And Their Efficient Implementations, Muharrem Tolga Sakalli, Sedat Akleylek, Kemal Akkanat, Vincent Rijmen

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we explicitly define the automorphisms of MDS matrices over the same binary extension field. By extending this idea, we present the isomorphisms between MDS matrices over $\mathbb{F}_{2^{m}}$ and MDS matrices over $\mathbb{F}_{2^{mt}}$, where $t \ge 1$ and $m>1$, which preserves the software implementation properties in view of XOR operations and table lookups of any given MDS matrix over $\mathbb{F}_{2^{m}}$. Then we propose a novel method to obtain distinct functions related to these automorphisms and isomorphisms to be used in generating isomorphic MDS matrices (new MDS matrices in view of implementation properties) using the existing ones. The …

Wideband Patch Array Antenna Using Superstrate Configuration For Future 5gapplications, Sidra Farhat, Farzana Arshad, Yasar Amin, Jonathan Loo

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, four distinct antenna configurations for future-centric 5G applications are proposed. Initially, a single rectangular patch is designed to operate at the frequency of 28 GHz while maintaining a wide operational band. Performance of the antenna is improved by incorporating an array of identical rectangular elements resulting in a higher gain and wider bandwidth. The proposed arrangement consists of three rectangular elements realized using 0.508-mm thick Rogers RT/Duroid 5880 laminate. The bandwidth is further enhanced by increasing the number of radiating elements in the array from three to five. Evolution of the proposed design is concluded by stacking …

Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that …

Power-Over-Tether Uas Leveraged For Nearly Indefinite Meteorological Data Acquisition In The Platte River Basin, Daniel Rico, Carrick Detweiler, Francisco Munoz-Arriola

School of Computing: Conference and Workshop Papers

The integration of unmanned aerial systems (UASs) has increased in the field of agriculture. These systems can provide data that was previously difficult to obtain to help increase efficiency and production. Typical commercial off the shelf (COTS) UASs have significant limitations in the form of small payloads, and short flight times which inhibit their ability to provide significant quantities of useful data. We present the development of a novel power-over-tether UAS that leverages the physical presence of the tether to integrate sensors at multiple altitudes along the tether. The UAS can acquire data nearly indefinitely to sense atmospheric conditions and …

Predicting Early Indicators Of Cognitive Decline From Verbal Utterances, Swati Padhee, Anurag Illendula, Megan Sadler, Valerie L. Shalin, Tanvi Banerjee, Krishnaprasad Thirunarayan, William Romine

Computer Science and Engineering Faculty Publications

Dementia is a group of irreversible, chronic, and progressive neurodegenerative disorders resulting in impaired memory, communication, and thought processes. In recent years, clinical research advances in brain aging have focused on the earliest clinically detectable stage of incipient dementia, commonly known as mild cognitive impairment (MCI). Currently, these disorders are diagnosed using a manual analysis of neuropsychological examinations. We measure the feasibility of using the linguistic characteristics of verbal utterances elicited during neuropsychological exams of elderly subjects to distinguish between elderly control groups, people with MCI, people diagnosed with possible Alzheimer's disease (AD), and probable AD. We investigated the performance …

Hierarchical Anomaly Detection For Time Series Data, Ryan E. Sperl

Browse all Theses and Dissertations

With the rise of Big Data and the Internet of Things, there is an increasing availability of large volumes of real-time streaming data. Unusual occurrences in the underlying system will be reflected in these streams, but any human analysis will quickly become out of date. There is a need for automatic analysis of streaming data capable of identifying these anomalous behaviors as they occur, to give ample time to react. In order to handle many high-velocity data streams, detectors must minimize the processing requirements per value. In this thesis, we have developed a novel anomaly detection method which makes use …

การสกัดตารางและรายการบนเว็บเป็นอาร์ดีเอฟ, จุลเทพ นันทขว้าง

Chulalongkorn University Theses and Dissertations (Chula ETD)

ทุกวันนี้ ลิงก์เดต้าได้เติบโตเพิ่มขึ้นอย่างรวดเร็วตามการเติบโตของเว็บ นอกเหนือจากข้อมูลใหม่ที่สร้างขึ้นในรูปแบบซีแมนติกโดยเฉพาะ ส่วนหนึ่งมาจากการแปลงข้อมูลโครงสร้างที่มีอยู่ให้อยู่ในรูปแบบของข้อมูลเปิดระดับห้าดาว อย่างไรก็ตามยังคงมีข้อมูลจำนวนมากในรูปแบบโครงสร้างและกึ่งโครงสร้าง ตัวอย่างเช่นตารางและรายการซึ่งเป็นรูปแบบหลักที่มนุษย์ใช้อ่าน ยังรอการแปลงอยู่ งานวิจัยนี้กล่าวถึงงานวิจัยต่าง ๆ ที่เกี่ยวกับการแปลงตารางและรายการมาเป็นข้อมูลในรูปแบบต่าง ๆ เพื่อให้เครื่องสามารถอ่านได้ นอกจากนี้ยังเสนอวิธีการในการแปลงตารางและรายการเป็นรูปแบบ Resource Description Framework และยังคงเก็บโครงสร้างต้นฉบับที่จำเป็นไว้อย่างละเอียด ซึ่งทำให้สามารถที่จะสร้างข้อมูลโครงสร้างเดิมกลับมาได้ ระบบ TULIP ถูกสร้างขึ้นเพื่อเป็นเครื่องมือสำหรับการพัฒนาซีแมนติกเว็บ วิธีการที่เสนอมีความยืดหยุ่นมากกว่าเมื่อเทียบกับงานอื่น ๆ เดต้าโมเดลของ TULIP สามารถรองรับการเก็บข้อมูลต้นฉบับอย่างครบถ้วน และสามารถนำมาแสดงใหม่ในมุมมองที่แตกต่างไปจากเดิม เครื่องมือนี้สามารถใช้สร้างข้อมูลจำนวนมหาศาลสำหรับเครื่องคอมพิวเตอร์เพื่อให้ใช้งานได้กว้างมากขึ้นกว่าเดิม

การสรุปใจความสำคัญของข้อความแบบสกัดสำหรับข่าวท่องเที่ยวภาษาไทย, ศรัญญา นาทองห่อ

Chulalongkorn University Theses and Dissertations (Chula ETD)

ปัจจุบันเทคโนโลยีทางด้านคอมพิวเตอร์มีความสำคัญต่อการดำเนินชีวิตประจำวันของมนุษย์เป็นอย่างมากและยังถือว่าเป็นเครื่องมือที่ใช้ในการอำนวยความสะดวกให้แก่มนุษย์มากมายโดยเฉพาะทางด้านการสื่อสารผ่านสังคมออนไลน์ เพื่อลดเวลาในการอ่านข่าวหรืออ่านบทความและข่าวออนไลน์ต่างๆ จากการวิจัยที่ผ่านมามีการศึกษาและพัฒนาการสรุปใจความสำคัญของภาษาไทยเป็นจำนวนมาก ในงานวิจัยนี้ได้นำเสนอวิธีการสรุปใจความสำคัญจากข่าวการท่องเที่ยวภาษาไทย 2 วิธีคือการเลือกประโยคจากการจัดกลุ่มประโยคด้วยเคมีนและการเลือกประโยคด้วยวิธีหาคำสำคัญประโยคจากหัวข้อข่าว โดยมีการพัฒนาและสร้างคลังข้อมูลรายการคำประสมเพื่อช่วยเพิ่มประสิทธิภาพในการตัดคำ โดยการทดลองนี้ใช้ข้อมูลข่าวการท่องเที่ยวไทย ทั้งหมด 400 ข่าวสำหรับใช้ทดลองในการสรุปใจความสำคัญ และ 5,000 ข่าวสำหรับการสร้างคลังข้อมูลรายการคำประสม การวัดประสิทธิภาพของวิธีการที่นำเสนอ มีการวัดประสิทธิภาพการสรุปใจความสำคัญโดยการเปรียบเทียบผลจากการสรุปที่ได้จากผู้เชี่ยวชาญด้านภาษาไทยเทียบกับผลสรุปที่ได้จากวิธีการที่นำเสนอ จากงานวิจัยนี้ในขั้นตอนการสร้างคำประสมได้คำประสมทั้งหมด จำนวน 2,340 คำ ผลการทดลองพบว่าวิธีตัดคำด้วยคัตคำร่วมกับตัดคำประสมได้ผลดีกว่าการตัดคำจากคัตคำเพียงอย่างเดียว และการสรุปใจความสำคัญโดยใช้การคำนวณค่าน้ำหนักของคำสำคัญโดยหาค่าความถี่ของคำจากหัวข้อข่าวเพียงอย่างเดียวและเลือกประโยคเรียงลำดับจากผลรวมความถี่ของคำสำคัญจากหัวข้อข่าวมีประสิทธิภาพและความแม่นยำสูงสุดโดยมีค่าความแม่นยำ ค่าความระลึกและค่าวัดประสิทธิภาพอยู่ที่ 0.8097 0.8367 และ 0.8216 ตามลำดับและเมื่อใช้คัตคำร่วมกับการตัดคำแบบเอ็นแกรมโดยวิธีการสรุปใจความสำคัญแบบเดียวกันได้ค่าความแม่นยำ ค่าความระลึกและค่าวัดประสิทธิภาพอยู่ที่ 0.8119 0.8398 และ 0.8242 ตามลำดับที่อัตราการบีบอัดร้อยละ 20

Pig Pose Estimation Based On Extracted Data Of Mask R-Cnn With Vgg Neural Network For Classifications, Sang Kwan Lee

Electronic Theses and Dissertations

This paper proposes a pig pose estimation operating with Region Proposal Network (RPN) of Mask Region based Convolutional Neural Network (Mask R-CNN) and Visual Geometry Group (VGG) Neural Network (NN). Object pose estimations generates from the associations of different key points. Key points could be explained as specific location of an object such as different joints of a human body or joints of different object. Hourglass network is one of a NN delivering key points of an object. Associating the different key points with the hourglass network results could be represented as instance-level detection [3]. However, the instance-level detection shows …

Novel Computational Approaches For Multidimensional Brain Image Analysis, Harish Raviprakash

Electronic Theses and Dissertations, 2020-2023

The overall goal of this dissertation is focused on addressing challenging problems in 1D, 2D/3D and 4D neuroimaging by developing novel algorithms that combine signal processing and machine learning techniques. One of these challenging tasks is the accurate localization of the eloquent language cortex in brain resection pre-surgery patients. This is especially important since inaccurate localization can lead to diminshed functionalities and thus, a poor quality of life for the patient. The first part of this dissertation addresses this problem in the case of drug-resistant epileptic patients. We propose a novel machine learning based algorithm to establish an alternate electrical …

Development Of A Modeling Algorithm To Predict Lean Implementation Success, Richard Charles Barclay

Doctoral Dissertations

”Lean has become a common term and goal in organizations throughout the world. The approach of eliminating waste and continuous improvement may seem simple on the surface but can be more complex when it comes to implementation. Some firms implement lean with great success, getting complete organizational buy-in and realizing the efficiencies foundational to lean. Other organizations struggle to implement lean. Never able to get the buy-in or traction needed to really institute the sort of cultural change that is often needed to implement change. It would be beneficial to have a tool that organizations could use to assess their …

Studying The Effects Of Various Process Parameters On Early Age Hydration Of Single- And Multi-Phase Cementitious Systems, Rachel Cook

Doctoral Dissertations

”The hydration of multi-phase ordinary Portland cement (OPC) and its pure phase derivatives, such as tricalcium silicate (C₃S) and belite (ß-C₂S), are studied in the context varying process parameters -- for instance, variable water content, water activity, superplasticizer structure and dose, and mineral additive type and particle size. These parameters are studied by means of physical experiments and numerical/computational techniques, such as: thermodynamic estimations; numerical kinetic-based modelling; and artificial intelligence techniques like machine learning (ML) models. In the past decade, numerical kinetic modeling has greatly improved in terms of fitting experimental, isothermal calorimetry to kinetic-based modelling …

Deep Learning For Digitized Histology Image Analysis, Sudhir Sornapudi

Doctoral Dissertations

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In …

Computational Model For Neural Architecture Search, Ram Deepak Gottapu

Doctoral Dissertations

"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats …

Leveraging Peer-To-Peer Energy Sharing For Resource Optimization In Mobile Social Networks, Aashish Dhungana

Theses and Dissertations

Mobile Opportunistic Networks (MSNs) enable the interaction of mobile users in the vicinity through various short-range wireless communication technologies (e.g., Bluetooth, WiFi) and let them discover and exchange information directly or in ad hoc manner. Despite their promise to enable many exciting applications, limited battery capacity of mobile devices has become the biggest impediment to these appli- cations. The recent breakthroughs in the areas of wireless power transfer (WPT) and rechargeable lithium batteries promise the use of peer-to-peer (P2P) energy sharing (i.e., the transfer of energy from the battery of one member of the mobile network to the battery of …

Two Novel Radar Detectors For Spiky Sea Clutter With The Presence Of Thermal Noise And Interfering Targets, Nouh Guidoum, Faouzi Soltani, Amar Mezache

Turkish Journal of Electrical Engineering and Computer Sciences

In the context of noncoherent detection and high-resolution maritime radar system with low grazing angle, new Constant False Alarm Rate (CFAR) decision rules are suggested for two Compound Gaussian (CG) clutters namely: The K distribution and the Compound Inverse Gaussian (CIG) distribution, which are considered among the most appropriate models for sea clutter. The proposed decision rules are then modified to deal with the presence of thermal noise and interfering targets. The proposed detectors are investigated on the basis of synthetic data as well as real data of the IPIX radar database. The obtained results exhibit a high probability of …

Detection Of Hand Osteoarthritis From Hand Radiographs Using Convolutionalneural Networks With Transfer Learning, Kemal Üreten, Hasan Erbay, Hadi̇ Hakan Maraş

Turkish Journal of Electrical Engineering and Computer Sciences

Osteoarthritis is the most common type of arthritis. Hand osteoarthritis leads to specific structural changes in the joints, such as asymmetric joint space narrowing and osteophytes (bone spurs). Conventional radiography has traditionally been the primary method of visualizing these structural changes and diagnosing osteoarthritis. We aimed to develop a computerized method that is capable of determining the structural changes seen in radiography of the hand and to assist practitioners in interpreting radiographic changes and diagnosing the disease. In this retrospective study, transfer-learning-based convolutional neural networks were trained on a randomly selected dataset containing 332 radiography images of hands from an …

Wind Power Forecasting Methods Based On Deep Learning: A Survey, Xing Deng, Haijian Shao, Chunlong Hu, Dengbiao Jiang, Yingtao Jiang

Electrical & Computer Engineering Faculty Research

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of …

Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin

Browse all Theses and Dissertations

Features and attributes that describe an event (disasters, social movements, etc.) are heterogeneous in nature. For virtually all events that impact humans, technology enables us to capture a large amount and variety of data from many sources, including humans (i.e., social media) and sensors/internet of things (IoTs). The corresponding modalities of data include text, imagery, voice and video, along with structured data such as gazetteers (i.e., location-based data) and government and statistical data. However, even though there is often an abundance of information produced, this information is fragmented across the various modalities and sources. The DisasterRecord system aims to provide …

Towards Interpretable And Reliable Deep Neural Networks For Visual Intelligence, Ning Xie

Browse all Theses and Dissertations

Deep Neural Networks (DNNs) are powerful tools blossomed in a variety of successful real-life applications. While the performance of DNNs is outstanding, their opaque nature raises a growing concern in the community, causing suspicions on the reliability and trustworthiness of decisions made by DNNs. In order to release such concerns and towards building reliable deep learning systems, research efforts are actively made in diverse aspects such as model interpretation, model fairness and bias, adversarial attacks and defenses, and so on. In this dissertation, we focus on the research topic of DNN interpretations for visual intelligence, aiming to unfold the black-box …

Process Data Analytics Using Deep Learning Techniques, Majid Moradi Aliabadi

Wayne State University Theses

In chemical manufacturing plants, numerous types of data are accessible, which could be process operational data (historical or real-time), process design and product quality data, economic and environmental (including process safety, waste emission and health impact) data. Effective knowledge extraction from raw data has always been a very challenging task, especially the data needed for a type of study is huge. Other characteristics of process data such as noise, dynamics, and highly correlated process parameters make this more challenging.

In this study, we introduce an attention-based RNN for multi-step-ahead prediction that can have applications in model predictive control, fault diagnosis, …

Context-Aware System For Glycemic Control In Diabetic Patients Using Neural Networks, Owais Bhat, Dawood A. Khan

Turkish Journal of Electrical Engineering and Computer Sciences

Diabetic patients are quite hesitant in engaging in normal physiological activities due to difficulties associated with diabetes management. Over the last few decades, there have been advancements in the computational power of embedded systems and glucose sensing technologies. These advancements have attracted the attention of researchers around the globe developing automatic insulin delivery systems. In this paper, a method of closed-loop control of diabetes based on neural networks is proposed. These neural networks are used for making predictions based on the clinical data of a patient. A neural network feedback controller is also designed to provide a glycemic response by …

A Power And Area Efficient Approximate Carry Skip Adder For Error Resilient Applications, Sujit Patel, Bharat Garg, Shireesh Kumar Rai

Turkish Journal of Electrical Engineering and Computer Sciences

The compute-intensive multimedia applications on portable devices require power and area efficient arithmetic units. The adder is a prime building block of these arithmetic units and limits the overall performance. Therefore, this paper analyzes the logic operations of the state-of-the-art adders and presents a novel low complexity adder segment with new carry prediction logic by removing the redundant logic and sharing the common operations. Further, a new power and area efficient approximate carry skip (PAEA-CSK) adder is proposed using the novel adder segment. The effectiveness of the proposed PAEA-CSK adder is evaluated and compared over the existing adders by implementing …

Rule Extraction And Performance Estimation By Using Variable Neighborhoodsearch For Solar Power Plant In Konya, Yusuf Uzun, Muci̇z Özcan

Turkish Journal of Electrical Engineering and Computer Sciences

The use of renewable energy sources in the production of electricity has become inevitable in order to reduce the greenhouse gases left in the atmosphere that cause the Earth to warm up. Although countries on a national basis have implemented a number of policies to support electricity generated from renewable energy sources, investments to produce electricity without a license on a local basis are not desirable. Those who want to invest medium and small scale for the most reason expect that this work will be supported by real data. Although the electricity generated by renewable investments is generated by simulation …

Robust Optimal Operation Of Smart Distribution Grids With Renewable Basedgenerators, Omid Zare, Sadjad Galvani, Murtaza Farsadi

Turkish Journal of Electrical Engineering and Computer Sciences

Modern distribution systems are equipped with various distributed energy resources (DERs) because of the importance of local generation. These distribution systems encounter more and more uncertainties because of the ever-increasing use of renewable energies. Other sources of uncertainty, such as load variation and system components? failure, will intensify the unpredictable nature of modern distribution systems. Integrating energy storage systems into distribution grids can play a role as a flexible bidirectional source to accommodate issues from constantly varying loads and renewable resources. The overall functionality of these modern distribution systems is enhanced using communication and computational abilities in smart grid frameworks. …

Assessment Of Environmental Factors Affecting Software Reliability: A Survey Study, Alper Özcan, Çağatay Çatal, Cengi̇z Toğay, Bedi̇r Teki̇nerdoğan, Emrah Dönmez

Turkish Journal of Electrical Engineering and Computer Sciences

Currently, many systems depend on software, and software reliability as such has become one of the key challenges. Several studies have been carried out that focus on the impact of external environmental factors that impact software reliability. These studies, however, were all carried out in the same geographical context. Given the rapid developments in software engineering, this study aims to identify and reinvestigate the environmental factors that impact software reliability by also considering a different context. The environmental factors that have an impact on software reliability as reported in earlier studies have been analyzed and synthesized. Subsequently, a survey study …

Prominent Quality Attributes Of Crisis Software Systems: A Literature Review, Ahmet Ari̇f Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

Developing software systems to meet user-demanded functionality is critical. Achieving the design goals by providing the needed functionality is a necessary task, and it is about figuring out a proper set of quality attributes and implementing each one by reflecting a complete set of quality attributes. This study presents popular quality attributes of crisis software systems by conducting a literature review. Each crisis software system has been studied by concentrating on crisis management phases where the system is used, design purposes, and the data processing style. The findings of this research shed light on the crisis software development process by …

A Mechanism Of Qos Differentiation Based On Offset Time And Adjusted Burstlength In Obs Networks, Viet Minh Nhat Vo, Trung Duc Pham, Thanh Chuong Dang, Van Hoa Le

Turkish Journal of Electrical Engineering and Computer Sciences

Quality of service (QoS) differentiation is an integral component of any networking system, particularly, with the current and future great diversity of users? applications and their manifold requirements. In optical burst switching (OBS) networks, there are two approaches for QoS differentiation: one is based on offset time and the other is based on burst length. This paper presents a mechanism of QoS differentiation based on both offset time and burst length, in which the offset times are calculated to achieve a complete isolation of data loss between priority classes and the burst length is adaptively adjusted according to the feedbacked …

Diagnosis Of Speed Sensor Faults In An Induction Machine Based On A Robustadaptive Super-Twisting Observer, Mohammed Zakaria Kari, Abdelkader Mechernene, Sidi Mohammed Meliani, Ibrahim Guenoune

Turkish Journal of Electrical Engineering and Computer Sciences

The present paper aims to determine a robust sensor fault-tolerant controller based on fuzzy logic using a robust adaptive super-twisting observer for the control of an induction machine and an inverter set by a state estimation method. The speed sensor is considered in the present case. The modular structure of the fault-tolerant control (FTC) scheme allows integrating this sensor within the existing closed-loop system, and the observer can therefore be designed independently. This article presents a new method to develop a fuzzy decision system that provides faulttolerant control. This paper also aims at detecting the mechanical speed sensor faults. The …