Computer-aided selection identification systems

Scheduling communications contact between a satellite and tracking station (satellite support) is difficult and time consuming. It requires specialized knowledge and expertise. The two main objectives are to minimize the total number of supports required for each satellite and to maximize the scheduling flexibility provided to the tracking station schedulers. We developed a prototype expert system for scheduling satellite supports for satellites in the Global Positioning System. The prototype used Texas Instruments' Personal Consultant Plus software and incorporated a number of unique search routines and graphics. Because of the success of the prototype expert system, the Air Force has sponsored several follow-on applications of expert systems to scheduling satellite supports.

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

Expert systems, in addition to performing diagnostic, consultative, and decision-making functions, can be effective data collection devices. These statistical data collection by-products may apply in various situations where information about the user and the operation of the system are valuable to the developers. Little research has been done to investigate how statistics may be kept and used by an expert system. The authors present a method for by-product data collection from an expert system and show how the collected data can be used to define the user, clarify the operation of the system, and enhance the system itself. A combination of SPSS/PC+ and Personal Consultant Plus packages on an IBM-style PC demonstrate the wide-spread applicability of using expert system technology to collect and analyze by-product data. Thus, the technology is easily available to many who could benefit from larger volumes of data collected in a consistent fashion. >

Graphics as an expert system user interfacing media

This is a combination of YOLO (you only look once) target recognition detection algorithm and AIS (Automatic Identification System) automatic ship identification system to assist vessels in inland navigation to carry out vessels and obstacle avoidance. This system proposes a ship-assisted collision avoidance system for ship collision accidents caused by a narrow inland waterway and complex navigation channel. The system can process the image and video information acquired by the high-definition camera mounted on the ship, and frame and mark the ships in the current sea area, and combine the AIS system to realize the visualization of the ship information and improve the global view. The experiment shows that the system can effectively identify and frame the ship and realize the visualization of AIS information, and the transformed image and video information can be used for decision-making by the ship's driver.

The study in this paper introduces the development of a knowledge-based expert system that helps to determine the suitability of FGD by-product in embankment construction. The expert system shell Personal Consultant Plus was used for this purpose. The knowledge base in the Embankment Construction Expert System (ECES) consists of a root frame and four subframes, and accommodates the production rules obtained from laboratory tests as well as an extensive literature review. An external program to calculate the embankment settlement was integrated into the system. A case study is presented to demonstrate the use of ECES. This study provides the embankment designer with a useful tool for making decisions on FGD by-product application as an embankment material.

This paper reports the design, implementation, and evaluation of a research work for developing an automatic person identification system using hand signatures biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment‥ In order to train and test the developed automatic person identification system, an in-house hand signatures database is created, which contains hand signatures of 100 persons (50 males and 50 females) each of which is repeated 30 times. Therefore, a total of 3000 hand signatures are collected. The collected hand signatures have gone through pre-processing steps such as producing a digitized version of the signatures using a scanner, converting input images type to a standard binary images type, cropping, normalizing images size, and reshaping in order to produce a ready-to-use hand signatures database for training and testing the automatic person identification system. Global features such as signature height, image area, pure width, and pure height are then selected to be used in the system, which reflect information about the structure of the hand signature image. For features training and classification, the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network (ANN) is used. This paper also investigates the effect of the persons' gender on the overall performance of the system. For performance optimization, the effect of modifying values of basic parameters in ANN such as the number of hidden neurons and the number of epochs are investigated in this work. The handwritten signature data collected from male persons outperformed those collected from the female persons, whereby the system obtained average recognition rates of 76.20% and74.20% for male and female persons, respectively. Overall, the handwritten signatures based system obtained an average recognition rate of 75.20% for all persons.

Aplicación para el estudio de implantación de un sistema no satelitario de identificación de buques

Chagas disease (CD) is a vector-borne zoonotic disease affecting large parts of the world. It is imposing a tremendous social burden on public health and ranks as one of the most severe threats to human health. CD is often transmitted to humans by the feces of insects called triatomine or kissing bugs. The diagnosis of CD can be performed at any stage of the disease and involves the analysis of clinical, epidemiological, and laboratory data. The CD has two different phases, acute phase and chronic phase. Since controlling and treating CD is easier in the early stages, detecting it in the acute phase plays an essential role in overcoming and controlling it. There are many clinical trials dedicated to this problem, but progress in applicational research (automatic identification) has been slower. Due to this shortcoming and the importance of this problem, this research is dedicated to present two automatic CD vector identification systems that classify several different vectors of kissing bugs with an acceptable and promising identification rate. Our proposed methods are composed of preprocessing, feature extraction, and classification phases. Principal component analysis (PCA) is utilized for feature extraction and Random Forrest (RF) and Support Vector Machine (SVM) are employed in the classification stages. A dataset consisting of more than two thousand kissing bug images is used as input of our methods. The accuracy for the first proposed approach, namely PCA-SVM, is 87.62% for 410 images of 12 Mexican and 75.26% for 1620 images of 39 Brazilian species. The second proposed approach, namely PCA-RF, has an accuracy of 100% for both Brazilian and Mexican species. We achieved perfect results with the PCA-RF method. Our results are promising and outperform the results of other available developed automatic identification systems for CD vectors.

Three-dimensional Feature Recognition-based Automated Patient Treatment Mismatch Verification System for Radiation Therapy

Maritime traffic pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime traffic model. In this paper, we propose a framework of maritime traffic pattern extraction from vessel AIS information, which learns a traffic pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, traffic pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts traffic pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective traffic pattern. The proposed method strongly supports the further research on maritime traffic pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.

The AIS (Automatic Identification System) became a standard under the International Convention for the Safety of Life at Sea (SOLAS) in December 2001. Using the AIS transponder, the vessel will report its position, identity heading and other information to other ship traffic in the same area. The AIS is a significant contribution to the control and safety of shipping. Transport Canada (TC) is a pioneer internationally in using the AIS (R4A airborne AIS transponder) integrated with the remote sensing system (MSS 6000 from Swedish Space Corporation) on board their aircraft, in order to add the information on ship identity and voyage to the information from other sensors. The AIS integration provides a quantum leap in the effectiveness of the surveillance mission. In the Canadian system integration, detailed vessel information obtained through the AIS system is displayed and correlated with information received from other sensors. All information is also presented together with a high-resolution digital map including nautical chart data. The AIS provides the operators with vessel voyage and identity information. It is very useful for vectoring the aircraft to areas where ships have been reported. The advent of AIS simplifies surveillance procedures and makes each hour of surveillance more productive. During last year TC reported that over 9700 vessels were over flown. It is anticipated that over 20,000 AIS targets will be acquired using AIS in 2007–2008. The experience from Canada also indicates an outstanding VHF range. During missions, vessels have been identified as far away as 200 nautical miles (n.m.) from the aircraft. The AIS information together with other sensor images gives a comprehensive image that will dramatically improve the chance to tie a ship to a detected illegal oil spill. Receiving ship identity information will enable aircrews to conduct covert operations and contact the suspect ship from far standoff distances. This will also act as a deterrent to potential polluters. Integrating AIS within the operational requirements of surveillance programs, dedicated to minimizing the adverse effects of shipping on the marine environment, will make a significant contribution towards improving the effectiveness of surveillance missions.

국제해사기구(International Maritime Organization, IMO)에서는 선박의 해상 충돌을 방지하기 위하여 SOLAS의 요구조건에 따라 모든 선박에 대하여 선박자동식별장치(Automatic Identification System, AIS)를 의무적으로 장착하도록 권고하고 있다. AIS를 사용하면 타선에 대한 제원 및 항행정보의 획득이 가능하여 충돌방지뿐만 아니라 광역관제, 조난 선박의 탐색구조 등 안전관리에도 활용이 가능하다. 본 논문에서는 AIS 장비를 사용하여 ADS-B (Automatic Dependent Surveillance - Broadcast) 개념 기반의 항공감시용 송수신기를 구현한 결과에 대하여 살펴보도록 한다. 기본적으로 AIS는 해상에서 사용되기 때문에 고도 정보를 사용하지 않지만, AIS 장비 내부에 사용하는 GPS (Global Positioning System) 칩셋에서 제공하는 고도 정보의 추출이 가능하다. 고도 정보를 포함한 감시 메시지 형식을 정의하고, SOTDMA (Self-Organizing Time Division Multiple Access) 방식을 개선하여 ADS-B 송수신기를 구현하였다. 적용 결과를 확인하기 위하여 지상시험 및 비행시험을 수행하였다. International Maritime Organization (IMO) recommends the installation of an Automatic Identification System (AIS) according to requirements by SOLAS to avoid maritime collision. AIS provides traffic information of other ships that may be used for maritime traffic control, SAR (Search and Rescue) and collision avoidance to apply safety management. In this paper, preliminary results to implement an aeronautical surveillance transceiver using AIS transceiver based on ADS-B concepts are described. Although altitude information is not required for AIS since the AIS is operated at MSL (Mean Sea Level), altitude information can be extracted by a GPS (Global Positioning System) chip-set in the AIS transceiver. ADS-B transceiver is implemented by defining a surveillance message format including the altitude information and modifying SOTDMA (Self-Organizing Time Division Multiple Access) protocol. Ground tests and flight tests are performed to validate the implementation results.

The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset.

The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high interest. Accurate matching of ships detected in SAR images with AIS data requires compensation of the azimuth offset, which depends on the ship’s velocity. The existing procedures interpolate the route information gathered by AIS to estimate the ship’s velocity at the epoch of the SAR data, to remove the offset. Matching accuracy is limited by interpolation errors and AIS route information unavailability or uncertainties. This paper proposes the use of SAR-based ship velocity estimations to improve the integration of AIS and SAR data. A case study has been analyzed, in which the method has been tested on TerraSAR-X images collected over the Gulf of Naples, Italy. Presented results show that the matching is improved with respect to standard procedures. The proposed method limits the distance between the AIS report and the SAR-based detection to less than 150 m, which is in line with maritime surveillance needs.