International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / At present, there is no means of instrument direct measurement to frequency deviation when it is up 500kHz. But the frequency deviation of high bit rate telemetry transmitter is 700kHz or more. In this paper, an indirect measurement method using spectrum analyzer and counter is put forward. It effectively solves the measurement problem of frequency deviation and frequency response of high bit rate telemetry transmitters. Measuring theory, summary of experiences and difficulties in measuring work, have been deeply studied with the viewpoint of how to avoid the limitation of different methods of measurement. Focused on the establishment of an automatic measuring system, expert system, skilled data and software of the system are studied in detail. The data for comparison is also supplied. Finally, the analysis to the measuring error and general uncertainty is given.

International Telemetering Conference Proceedings / October 26-29, 1987 / Town and Country Hotel, San Diego, California / In an Independent Research and Development (IR&D) effort, the Telemetry Systems Operation (TSO) of Computer Sciences Corporation (CSC) sought to determine the feasibility of using Artificial Intelligence (AI) techniques in a real-time processing environment. Specifically, the use of an expert system to assist in telemetry data acquisition processing was studied. A prototype expert system was implemented with the purpose of monitoring F15 Vertical Short Take Off and Landing (VSTOL) aircraft engine tests in order to predict engine stalls. This prototype expert system was implemented on a Symbolics 3670 symbolic processor using Inference Corporation's Artificial Reasoning Tool (ART) expert system compiler/generator. The Symbolics computer was connected to a Gould/SEL 32/6750 real-time processor using a Flavors, Inc. Bus Link for real-time data transfer.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In recent years the telemetry community has encountered a growing demand for bandwidth from users and a corresponding loss of spectrum. The Advanced Range Telemetry (ARTM) Program has responded to this situation with an initiative to develop, demonstrate, and improve the management and control of telemetry resources using demand assigned multiple access (DAMA) techniques. This initiative has proceeded along two paths. The first path is in the development of an expert system to facilitate the scheduling of telemetry missions and the deconfliction of their frequencies. This system emphasizes the graphical manipulation of mission data and uses a genetic algorithm to search for an optimal set of mission frequencies. The second path is the development of a bidirectional command and control link to remotely control and configure the frequency of a telemetry link. This link uses the simple network management protocol (SNMP) over a wireless Internet Protocol (IP) network implemented with Digital Communications Network System (DCNS) units.

This thesis reports the research efforts conducted in order to develop the Dynamic Response Recovery Tool. The DRRT was developed as a decision support tool under a holistic approach considering both emergency management research and transportation studies. The proposed system was assessed by a series of case studies in order to identify its efficiency and suitability for roading organisations. Knowledge developed from two novel research approaches are comprehensively described throughout the thesis. Initially, we report on the observation of three emergency exercises and two real events in New Zealand. This set of activities indicated the complex and dynamic environment in which emergency management takes place as well as organisational settings and management structures implemented to better respond and recover from disasters events. Additionally, a secondary approach was designed to overcome limitations identified in the observation method. In this context, a game-based scenario simulation was developed and conducted with twelve participants. With a focus in resource deployment decisions during emergencies, the game simulated an earthquake scenario in which participants had to allocate physical resources to fix damage created in a road network. Simulations indicated that Naturalistic Decision-making processes were used to respond to the scenario. Thus, resource allocation followed planning priorities defined previously the simulation, which further considered individual experiences and knowledge. Taking advantage from the findings achieved and knowledge developed by the observations and game simulations, the DRRT was designed using the conceptual background identified in the literature review. The DRRT was conceptualised as a logistics sub-system as part of the broad field of Disaster Management. In particular, the DRRT was geared towards supporting decision-making by providing procedural recommendations and identifying optimum physical deployment strategies. In order to assess the proposed system, an Information Technology application was built according to the DRRT’s specifications. A series of eleven individual and three group simulations was performed in order to assess the DRRT. Data collected through the application indicated that the DRRT enhanced decision-making during extreme events. In specific, case study participants using the system at greater levels achieved better decision-making accuracy than those disregarding completely or partially the system. Case studies also indicated that emergency management knowledge was represented by the application and its logistics model provided participants with vital information to optimise resource allocation.

Baigiamajame magistro darbe nagrinėjamas nekilnojamojo turto įmonių darbuotojų darbo efektyvumas ir jo didinimo galimybės panaudojant naujausias technologijas – biometrinę kompiuterio pelytę bei VGTU studentų ir dėstytojų sukurtą internetinę ekspertinę sistemą, duodančią patarimus darbuotojų našumui didinti. Darbe apibendrintai aprašomas Lietuvos ūkio darbo našumas, jo pasikeitimai per pastaruosius metus, bei veiksniai, turintys didžiausią įtaką darbo našumui. Taip pat darbe aprašomos biometrinės technologijos, apžvelgiamas jų panaudojimas nekilnojamojo turto sektoriuje ir galimybė jas pritaikyti darbo efektyvumui didinti. Atlikus stebėjimus biometrine kompiuterio pelyte ir nustačius didžiausią įtaką darbuotojų našumui darančius veiksnius, remiantis A. Maslowo poreikių teorija buvo sukurta ekspertinė darbo našumo didinimo sistema. Išanalizavus darbo su biometrine pelyte ir sukurta ekspertine sistema rezultatus, darbo gale pateikiamos darbo išvados ir pasiūlymai. / The labour productivity problem and possibility to improve labour productivity by using biometric technologies and web-based expert system, developed by students and academics of VGTU is analyzed in this thesis. Summarized description of the labour productivity in general, its progress during few past years in Lithuania and factors that make the biggest influence on the level of labour productivity are described. Also the work contains overview of the biometric systems, usage of them in real estate and the possibility to increase labour productivity. After making observations during work with biometric mouse and identifying factors that affect productivity the most, the expert system, based on the A.Maslows hierarchy of needs was developed. Conclusion and suggestions were made after performing the analysis of the results of working with the biometric mouse and web-based expert system.

A clinical trial is defined as a study conducted on a group of patients to determine the effect of a treatment. Assignment of patients to clinical trials is a data and labor intensive task. Usually, medical personnel manually check the eligibility of a patient for a clinical trial based on the patient's medical history and current medical condition. According to studies, most clinical trials are under-enrolled which negatively affects their effectiveness. We have developed web-based agents that can test the eligibility of patients for many clinical trials at once. We have tested various heuristics for optimizing cost and data entry needed in assigning patients to clinical trials. Testing eligibility of a patient for many clinical trials is only feasible if it is cost and data entry efficient. Agents with different heuristics were then tested on data from current breast cancer patients at the Moffitt Cancer Center. Results with different heuristics are compared with each other and with that of the clinicians. It is shown that cost savings are possible in clinical trial assignment. Also, less data entry is needed when probabilistic agents are used to reorder questions.

The bioreactor landfill is an emerging concept for solid waste management that has gained significant attention in the last decade. This technology employs specific operational practices to enhance the microbial decomposition processes in landfills. However, the unsupervised management and lack of operational guidelines for the bioreactor landfill, specifically leachate manipulation and recirculation processes, usually results in less than optimal system performance. Therefore, these limitations have led to the development of SMART (Sensor-based Monitoring and Remote-control Technology), an expert control system that utilizes real-time monitoring of key system parameters in the management of bioreactor landfills. SMART replaces conventional open-loop control with a feedback control system that aids the human operator in making decisions and managing complex control issues. The target from this control system is to provide optimum conditions for the biodegradation of the refuse, and also, to enhance the performance of the bioreactor in terms of biogas generation. SMART includes multiple cascading logic controllers and mathematical calculations through which the quantity and quality of the recirculated solution are determined. The expert system computes the required quantities of leachate, buffer, supplemental water, and nutritional amendments in order to provide the bioreactor landfill microbial consortia with their optimum growth requirements. Soft computational methods, particularly fuzzy logic, were incorporated in the logic controllers of SMART so as to accommodate the uncertainty, complexity, and nonlinearity of the bioreactor landfill processes. Fuzzy logic was used to solve complex operational issues in the control program of SMART including: (1) identify the current operational phase of the bioreactor landfill based on quantifiable parameters of the leachate generated and biogas produced, (2) evaluate the toxicological status of the leachate based on certain parameters that directly contribute to or indirectly indicates bacterial inhibition, and (3) predict biogas generation rates based on the operational phase, leachate recirculation, and sludge addition. The later fuzzy logic model was upgraded to a hybrid model that employed the learning algorithm of artificial neural networks to optimize the model parameters. SMART was applied to a pilot-scale bioreactor landfill prototype that incorporated the hardware components (sensors, communication devices, and control elements) and the software components (user interface and control program) of the system. During a one-year monitoring period, the feasibility and effectiveness of the SMART system were evaluated in terms of multiple leachate, biogas, and waste parameters. In addition, leachate heating was evaluated as a potential temperature control tool in bioreactor landfills. The pilot-scale implementation of SMART demonstrated the applicability of the system. SMART led to a significant improvement in the overall performance of the BL in terms of methane production and leachate stabilization. Temperature control via recirculation of heated leachate achieved high degradation rates of organic matter and improved the methanogenic activity.

The bioreactor landfill is an emerging concept for solid waste management that has gained significant attention in the last decade. This technology employs specific operational practices to enhance the microbial decomposition processes in landfills. However, the unsupervised management and lack of operational guidelines for the bioreactor landfill, specifically leachate manipulation and recirculation processes, usually results in less than optimal system performance. Therefore, these limitations have led to the development of SMART (Sensor-based Monitoring and Remote-control Technology), an expert control system that utilizes real-time monitoring of key system parameters in the management of bioreactor landfills. SMART replaces conventional open-loop control with a feedback control system that aids the human operator in making decisions and managing complex control issues. The target from this control system is to provide optimum conditions for the biodegradation of the refuse, and also, to enhance the performance of the bioreactor in terms of biogas generation. SMART includes multiple cascading logic controllers and mathematical calculations through which the quantity and quality of the recirculated solution are determined. The expert system computes the required quantities of leachate, buffer, supplemental water, and nutritional amendments in order to provide the bioreactor landfill microbial consortia with their optimum growth requirements. Soft computational methods, particularly fuzzy logic, were incorporated in the logic controllers of SMART so as to accommodate the uncertainty, complexity, and nonlinearity of the bioreactor landfill processes. Fuzzy logic was used to solve complex operational issues in the control program of SMART including: (1) identify the current operational phase of the bioreactor landfill based on quantifiable parameters of the leachate generated and biogas produced, (2) evaluate the toxicological status of the leachate based on certain parameters that directly contribute to or indirectly indicates bacterial inhibition, and (3) predict biogas generation rates based on the operational phase, leachate recirculation, and sludge addition. The later fuzzy logic model was upgraded to a hybrid model that employed the learning algorithm of artificial neural networks to optimize the model parameters. SMART was applied to a pilot-scale bioreactor landfill prototype that incorporated the hardware components (sensors, communication devices, and control elements) and the software components (user interface and control program) of the system. During a one-year monitoring period, the feasibility and effectiveness of the SMART system were evaluated in terms of multiple leachate, biogas, and waste parameters. In addition, leachate heating was evaluated as a potential temperature control tool in bioreactor landfills. The pilot-scale implementation of SMART demonstrated the applicability of the system. SMART led to a significant improvement in the overall performance of the BL in terms of methane production and leachate stabilization. Temperature control via recirculation of heated leachate achieved high degradation rates of organic matter and improved the methanogenic activity.

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

Every gas well ceases producing as reservoir pressure depletes. The usual liquid presence in the reservoir can cause further problems by accumulating in the wellbore and reducing production even more. There are a number of options in well completion to prevent liquid loading even before it becomes a problem. Tubing size and perforation interval optimization are the two most common methods. Although completion optimization will prevent liquid accumulation in the wellbore for a certain time, eventually as the reservoir pressure decreases more, the well will start loading. As liquid loading occurs it is crucial to recognize the problem at early stages and select a suitable prevention method. There are various methods to prevent liquid loading such as / gas lift, plunger lift, pumping and velocity string installation. This study set out to construct a decision tree for a possible expert system used to determine the best result for a particular gas well. The findings are tested to confirm by field applications as attempts of the expert system.