Determination of the requirement for transportation and technological machines by clusterization of oil and gas production departments

The article considers the analysis of production indicators of oil and gas production departments with the aim of clustering them for the subsequent determination of the need for automobiles and technological machines. The departments have different sizes, power, are in different conditions, are characterized by different performance indicators, but at the same time they are equipped with vehicles according to the same standards. This leads to problems in ensuring the uninterrupted transport and technological service of the main production. In a number of departments, situations arise when the planned number of transport and technological machines is not enough to perform technological operations for the repair or maintenance of wells. In this case, vehicles are sent from another sub-division, thereby limiting their own transport service capabilities. Fleet planning often takes place taking into account the historical conditions of the department, which is generally applicable for old departments with an established well stock, but practically does not work for newly formed departments with large volumes of newly commissioned wells and complicated production conditions. These subdivisions are equipped with vehicles in relation to existing workshops with similar indicators, which most often leads to an insufficient number of machines and downtime of the main production due to lack of machines. In this regard, it is necessary to search for and justify those production indicators of departments that determine their differentiation. The aim of the paper is to increase the efficiency of transport and technological service of oil and gas production facilities based on determining the patterns of influence of production indicators of production and gas shops on the need for transport and technological machines and developing, on this basis, differentiated standards for equipping units with vehicles. Using machine learning methods, the clustering of production units was carried out, and the factors that determine the distribution of departments into four groups were identified. The main factors include the stock of wells in the department and the degree of complexity of this stock. Groups are determined by the degree of change in these factors. The presented approach and the resulting distribution can be used as a basis for more efficient standardization of the needs of departments in automobiles and technological machines and also as part of decision support systems for vehicle fleet management

Vehicle classification

Práce se zabývá rozpoznáváním typu vozidel z obrazu pořízeného jednou kamerou. Obraz je pořízen vždy jako čelní pohled vozidla projíždějícího jedním konkrétním místem a to za různých světelných podmínek. Cílem práce je implementace metody klasifikace vozidel s ohledem na robustnost, spolehlivost a výpočetní náročnost. Řešení je implementováno v prostředí Microsoft Visual Studio 2013 s využitím knihovny OpenCV.This thesis deals with recognition of vehicles with image captured by one camera. The image is always taken as a front view of a vehicle passing one specific place in case of various lighting conditions. The aim is to implement classification method with regard to robustness, reliability and computional complexity. The method is implemented in Mircosoft Visual Studio 2013 using the OpenCV library.

Car make and model recognition under limited lighting conditions at night

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyCar make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when licence plate numbers cannot be identified or fake number plates are used. CMMR can also be used when automatic identification of a certain model of a vehicle by camera is required. The majority of existing CMMR methods are designed to be used only in daytime when most car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. This work identifies car make and model at night by using available rear view features. A binary classifier ensemble is presented, designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and licence plates from the rear view are extracted and used in the recognition process. The majority vote of individual classifiers, support vector machine, decision tree, and k-nearest neighbours is applied to verify a target model in the classification process. The experiments on 100 car makes and models captured under limited lighting conditions at night against about 400 other car models show average high classification accuracy about 93%. The classification accuracy of the presented technique, 93%, is a bit lower than the daytime technique, as reported at 98 % tested on 21 CMMs (Zhang, 2013). However, with the limitation of car appearances at night, the classification accuracy of the car appearances gained from the technique used in this study is satisfied

VEHICLE TYPE CLASSIFICATION USING PCA WITH SELF-CLUSTERING

Different conditions, such as occlusions, changes of lighting, shadows and rotations, make vehicle type classification still a challenging task, especially for real-time applications. Most existing methods rely on presumptions on certain conditions, such as lighting conditions and special camera settings. However, these presumptions usually do not work for applications in real world. In this paper, we propose a robust vehicle type classification method based on adaptive multi-class Principal Components Analysis (PCA). We treat car images captured at daytime and night-time separately. Vehicle front is extracted by examining vehicle front width and the location of license plate. Then, after generating eigenvectors to represent extracted vehicle fronts, we propose a PCA method with self-clustering to classify vehicle type. The comparison experiments with the state of art methods and real-time evaluations demonstrate the promising performance of our proposed method. Moreover, as we do not find any public database including sufficient desired images, we built up online our own database including 4924 high-resolution images of vehicle front view for further research on this topi