Applications of advanced data analysis and expert system technologies in the ATLAS Trigger-DAQ Controls framework

The ATLAS Trigger and Data Acquisition (TDAQ) infrastructure is responsible for filtering and transferring ATLAS experimental data from detectors to mass storage systems. It relies on a large, distributed computing system composed of thousands of software applications running concurrently. In such a complex environment, information sharing is fundamental for controlling applications behavior, error reporting and operational monitoring. During data taking, the streams of messages sent by applications and data published via information services are constantly monitored by experts to verify the correctness of running operations and to understand problematic situations. To simplify and improve system analysis and errors detection tasks, we developed the TDAQ Analytics Dashboard, a web application that aims to collect, correlate and visualize effectively this real time flow of information. The TDAQ Analytics Dashboard is composed of two main entities that reflect the twofold scope of the application. The first is the engine, a Java service that performs aggregation, processing and filtering of real time data stream and computes statistical correlation on sliding windows of time. The results are made available to clients via a simple web interface supporting SQL-like query syntax. The second is the visualization, provided by an Ajax-based web application that runs on client's browser. The dashboard approach allows to present information in a clear and customizable structure. Several types of interactive graphs are proposed as widgets that can be dynamically added and removed from visualization panels. Each widget acts as a client for the engine, querying the web interface to retrieve data with desired criteria. In this paper we present the design, development and evolution of the TDAQ Analytics Dashboard. We also present the statistical analysis computed by the application in this first period of high energy data taking operations for the ATLAS experiment.

A framework for the integration of expert systems with multimedia technologies

We are currently developing concepts and software tools designed to aid legal practitioners in the process of statutory interpretation: the process of determining the meaning of a statute or regulation and applying it to a particular set of facts. This is being attempted through the integration of database and expert system technologies. Case-Based Reasoning (CBR) is being used to model legal precedents while Rule-Based Reasoning (RBR) modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case-based databases with rule-based expert systems in the legal domain.

Background The VITEK®2 Advanced Expert System (AES) is an automated antibiotic susceptibility testing (AST) system which provides rapid AST data by interpreting and checking each MIC against a database of phenotypes and MIC distributions and infers a resistance mechanism. AES provides a level of confidence for auto-releasing reports of green (consistent), yellow (consistent with correction), and red (inconsistent, MIC pattern not matching any phenotype). This study evaluated VITEK®2 AES reports compared to broth microdilution (BMD) on a set of Pseudomonas aeruginosa (PA) and Acinetobacter spp. (ABC). Methods A total 148 PA and 150 ABC were tested. Susceptibility was tested by VITEK®2 (N802 and XN15 AST cards) and CLSI reference BMD against 13 antimicrobials for PA and 17 antimicrobials for ABC applying 2021 CLSI breakpoints. Molecular characterization of isolates was based on WGS or PCR of β-lactamase genes. Permeability and gene expression were not evaluated. Results Among 143 ABC, AES reports were green for 131 (91.6%), and yellow for 12 (8.4%) isolates. Green reports agreed with BMD for 131 isolates (100%) and could have been automatically reported without further review. Among yellow reports, AES corrections were acceptable for 11 isolates (91.7%). Among 40 carbapenem-producing ABC, AES reports were green for 39 isolates (97.5%) and yellow for one isolate (2.5%). Green reports agreed with BMD for 37 isolates (94.9%) and could have been automatically reported. Of 148 PA, AES reports were green for 125 (84.5%), yellow for 18 (12.2%), and red for 5 (3.4%) isolates. Of 112 PA with molecular data, 64 (57.1%), 38 (33.9%), 6 (5.4%), 3 (2.7%), and one (0.9%) contained genes for intrinsic β-lactamases only (WT), MBL, GES carbapenemase, KPC, and ESBL genes only, respectively. Overall, the AES accurately reported phenotypes for 97/112 PA (86.6%). Among the MBL-positive PA, 37/38 (97.4%) were reported as green and all showed AES phenotype agreement with the genotype. Among the WT PA, 51/64 (79.7%) were reported as green and 82.4% showed AES phenotype agreement with the genotype. Conclusion The use of VITEK®2 AES for the confident release of AST results without additional review could have a positive impact on time savings in the laboratory and aid in antimicrobial stewardship. Disclosures All Authors: No reported disclosures

Redex: The ranging equipment diagnostic expert system

This book presents an innovative approach to verifying and validating rule-based expert systems. It features a complete set of techniques and tools that provide a more formal, objective, and automated means of carrying out verification and validation procedures. Many of the concepts behind these procedures have been adapted from conventional software, while others have required that new techniques or tools be created because of the uniqueness of rule-based expert systems. Verification and Validation of Rule-Based Expert Systems is a valuable reference for electrical engineers, software engineers, artificial intelligence experts, and computer scientists involved with object-oriented development, expert systems, and programming languages.

Recently, the application of the conventional rule based expert system for disease risk determination in medical domains has increased. However, a major limitation to the effectiveness of the rule based expert system approach is the sharp boundary problem that leads to underestimation or overestimation of boundary cases, which ultimately affects the accuracy of their recommendation. In this paper, an expert driven approach is used to investigate the viability of a fuzzy expert system in the determination of risk associated with coronary heart disease with regards to the sharp boundary problem in rule based expert system.

Biomedical signal processing and data analysis play pivotal roles in the advanced medical expert system solutions. Signal processing tools are able to diminish the potential artifact effects and improve the anticipative signal quality. Data analysis techniques can assist in reducing redundant data dimensions and extracting dominant features associated with pathological status. Recent computational methods have greatly improved the effectiveness of signal processing and data analysis, to support the efficient point-of-care diagnosis and accurate medical decision-making. This editorial article highlights the research works published in the special issue of Computational Methods for Physiological Signal Processing and Data Analysis. The context introduces three deep learning applications in epileptic seizure detection, human exercise intensity analysis, and lung nodule CT image segmentation, respectively. The article also summarizes the research works on detection of event-related potential in the single-trial electroencephalogram (EEG) signals during the auditory tests, along with the methodology on estimating the generalized exponential distribution parameters using the simulated and real data produced under the Type I generalized progressive hybrid censoring schemes. The article concludes with perspectives and discussions on future trends in biomedical signal processing and data analysis technologies.

Development of a prototype expert system for identification and control of insect pests

The scope and limitations of first generation expert systems

Expert systems are a well-known and well-received technology. It was thought that the performance of a domain expert could not be duplicated by a machine. Expert systems technologies have shown this to be a false belief, and indeed have demonstrated how experts themselves can come to depend on expert systems. Expert systems enjoy widespread use in industrial domains and further uses are planned. The growth in acceptance has been explosive since about 1986. Continued rampant growth appears to depend on cracking the so-called knowledge acquisition bottleneck.The knowledge acquisition bottleneck limits the scalability of expert systems. While it is relatively straightforward to populate a small-scale knowledge base, it becomes more difficult to maintain consistency and validity as the knowledge base grows. Thus, it is important to automate the knowledge acquisition process. A by-product of this process is that any failure of the expert system will be “soft.”The question is, “What impact can machine learning technologies have on knowledge acquisition in the large?” The true test will be on prospective industrial applications in areas such as biology, education, geology, medicine, and scientific discovery. Machine learning technologies include expert systems, genetic algorithms, neural networks, random seeded crystal learning, or any effective combinations.Relevant subtopics include: Second generation expert systems — progress and prognosisRepertory GridsThe importance of symbolic and qualitative reasoningThe acquisition of fuzzy rulesThe best learning paradigm or combination of paradigmsImpact of machine learning on explanation systemsThe role of toy domains such as chessAutomatic programming revisitedApplications to computer vision, decision support systems, diagnosis, helpdesks, optimization, planning, scheduling, et al.Implementation issues using SIMD and MIMD platformsSources for joint sponsorshipForming industrial partnershipsForming alliances abroad

An optimized design of a matrix treatment involves fluid selection and acidizing simulations to predict the outcome of the treatment; Many matrix acidizing treatment designs and fluid selections have been successfully accomplished by utilizing expert system technology. However, none of these present a complete and optimized result (i.e., by utilizing the output of the expert system to predict the acidizing outcome using an acidizing numerical simulator). In the meantime, several acidizing computer simulation studies have been conducted separately. This paper presents a study which integrates the treatment design, particularly the fluid selection process, and acidizing simulation for sandstone formations. Required parameters for sandstone acidizing such as acid type, concentration, volume, and injection rate/pressure are first selected using an expert system. The output from the expert system is further used for the input to an acidizing numerical simulator (UTACID). A new sandstone acidizing reaction model, appropriate for a high-temperature environment, and anisotropic medium have been implemented into UTACID to enhance the performance of the simulator. The expert system and the simulator have been integrated to provide an optimization tool for sandstone acidizing treatment design and simulation.

Ameliorating the Pregnant Man problem: a verification tool for personal computer based expert systems

Diesel engine type of generators that uses fuel to produce electricity are common in many countries which suffer from inconsistency in electricity supply. To lower the cost, manufacturers tend to purchase secondhand generators to ensure constant supply of electricity during production. However, manufacturers do not have the proper skill to assess the health of second-hand generators. This, coupled with poor maintenance due to insufficient technical staff, the efficiency of generators is intolerably low and facing frequent breakdown. Such deficiencies not only waste expensive fuel, but also reduce the productivity due to sudden loss of electricity supply. Hence, there is an urgent need to have an automatic generator health assessment system so that the efficiency of generators can be increased and the chance of fatal breakdown can be minimized. This paper presents a sophisticated generator health assessment system that can automatically diagnose the current health status of an inspected generator even without a baseline for comparison purpose. Initially, we focused on diagnosing the faults generated from the combustion process. Our system uses low-cost encoders to assess the health of engines. Compared to conventional methods, such capability could only be achieved using expensive pressure sensors. Moreover, an expert system was built to diagnose engine faults automatically. With the help of this automatic and low-cost system, the operators will be alerted if any anomaly is occurring in the engine. Hence, our system makes the monitored generator more reliable in operation and saves expensive fuel wasted by improper combustion process.

An expert system for the selection of a suitable method for flow measurement in open channels