Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Detection and clustering of an Neutral Section faults using machine learning techniques for SMART railways

Abstract: Fault detection and diagnosis plays an important role particularly in railways were abnormal events are detected and a detailed root causes analysis is performed to prevent similar occurrence. The current method used to detect immediate and long-term faults is through foot inspections and inspection trolleys fitted with cameras proving to be inefficient and time consuming when analyzing the data. This paper examines the smart fault detection system on the overhead wires by applying machine learning techniques for accurate assessment of the neutral section before and after failure thereby grouping the events into fault bins. Modern computational intelligence has enabled the fault diagnostic and fault detection to be accurate from the data generated from the sensors. The interaction between the pantograph and contact wire will be monitored using accelerometers and non-contact infrared thermometer sensors were should there be a deviation from the normal signal spectrum it will be detected. The measured data from onsite will be conveyed to ThingSpeak for cloud computation thereby providing notifications in real-time which allows the end user to visualize, analyze and act on data online. A prototype has been built and tested which shows that the system works reasonably with data collected from sensors

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data.

Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness

First-Shot Unsupervised Anomalous Sound Detection With Unknown Anomalies Estimated by Metadata-Assisted Audio Generation

First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types are unseen in training. Existing methods often rely on the availability of normal and abnormal sound data from the target machines. However, due to the lack of anomalous sound data for the target machine types, it becomes challenging when adapting the existing ASD methods to the first-shot task. In this paper, we propose a new framework for the first-shot unsupervised ASD, where metadata-assisted audio generation is used to estimate unknown anomalies, by utilising the available machine information (i.e., metadata and sound data) to fine-tune a text-to-audio generation model for generating the anomalous sounds that contain unique acoustic characteristics accounting for each different machine types. We then use the method of Time-Weighted Frequency domain audio Representation with Gaussian Mixture Model (TWFR-GMM) as the backbone to achieve the first-shot unsupervised ASD. Our proposed FS-TWFR-GMM method achieves competitive performance amongst top systems in DCASE 2023 Challenge Task 2, while requiring only 1% model parameters for detection, as validated in our experiments.Comment: Submitted to ICASSP 202

Tüübituletus neljandat järku loogikavalemitele

Tänapäeval omavad nutiseadmed meie elus suurt rolli, eriti igapäevastes tegemistes. Sellepärast võib kaaluda nutitelefoni kui üht kõige huvitavamat andurit kujutamaks meie tegevusi ja meie ümbrust. Lisaks sellele on nutitelefonide arvutusjõudlus hüppeliselt kasvanud, mida kinnitavad nendes sisalduvad erinevad andurid nagu kiirendusmõõturid ja güroskoobid ning võimekus sooritada rohkem ülesandeid kui kunagi varem. Nende mugavuse ja madala hinna tõttu on nutitelefone hakatud kasutama kui kaasaskantavaid arvutusplatvorme autonoomsete sõidukite arenduses. Intelligentsete sõidukite süsteemide kriitiliseimaks probleemiks on turvalisus. Teekatte tuvastus on üks turvalise liiklemise põhikomponentidest. Enamik praeguseid lahendusi teekatte tuvastamiseks kasutavad erinevate sensorite nagu kaamerate ja LiDARite kokkusulatamist. See on küll efektiivne meetod, kuid tegemist on kallite anduritega ning mille kasutamine vajab auto enda modifitseerimist. Lõputöö pakub välja meetodi teekatte tuvastamiseks kasutades nutitelefonis oleva kiirendusmõõturi andmeid. See protsess kasutab ajaliselt jätjestatud kiirendusmõõturi andmeid, millele järgneb masiivne ajaliselt järjestatud tunnuste eraldamine ja valimine. Peale seda suunatakse eraldatud tunnused DeepSense närvivõrgu raamistikku, et teekate tuvastada. Meetod klassifitseerib kolme erinevat teekatte tüüpi: sile, munakivitee ja kruusatee. Põhjalik pakutud metoodika uurimine ja analüüs viiakse läbi kasutades üldlevinud masinõppe meetodeid nagu tugivektor-masinad, otsustusmets, täielikult ühendatud närvivõrgud ja konvulutioonteisendus närvivõrgud. Metoodikal põhinevad katsed näitavad, et pakutud lähenemine võimaldab tuvastada teekatte siledust väljapakutud kolme kategooriasse.Nowadays, Smart devices plays a big role in our lives, especially in our daily activities. Therefore, Smartphones can be considered as one of the most interesting sensor for depicting our activities and our surroundings. Furthermore, the computation power of smartphones has increased a lot recently as most of them have multiple sensors like accelerometers and gyroscopes. Besides, They are capable of processing more tasks than we ever imagined. Because of their advantages of convenience and low-cost, the portable computation platforms has been adopted in the development of autonomous vehicles. The most critical issue of the intelligent system assisted vehicles is that the safety problem. The recognition of the road surface is one of the components to ensure the safety drive. Most of the solutions use sensor fusion to recognize road surfaces such as combining cameras and LiDARs, which is costly for equipment and they usually need installations to re-equip existing cars, but these methods provide overall excellent results. This thesis proposes a method for recognizing the road surface based on using accelerometer data collected from smartphone. The process uses time series data collected from a smartphone’s accelerometer, followed by a massive time series feature extraction and selection. After that, the features are fed into trained DeepSense variant neural network framework to get the recognition of the road surfaces. The proposed method provides three classes recognition for smooth, bumpy and rough roads. Moreover, in this thesis we conducted a thorough evaluation and analysis of the proposed method by comparing it with conventional machine learning methods like SVM, random forest, fully connected neural network and convolutional neural network. The accuracy of the method in this thesis overmatch the compared examples. The road surface type will be classified into three categories which will indicate smoothness of the road surface