Intelligent Expert Research Articles

Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool.

Machining activities in recent times have shifted their focus towards tool life and tool wear. Cutting tools have been utilized on a daily basis and play a vital role in manufacturing industries. Prolonged and incessant operation of the cutting tool can lead to wear and tear of the component, thereby compromising the dimensional accuracy. The condition of a tool is estimated based upon the surface quality of the machined component, condition of the machine, and the rate of production. Maintaining the tool health plays a vital role in enhancing the productivity of manufacturing industries. Numerous efforts were experimented by the researchers to maintain the tool health condition. The drawbacks of conventional diagnostic techniques include requirement of high level of human intelligence and professional expertise on the field, which led the researchers to develop intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the condition of single point cutting tool. This article proposes the use of transfer learning technology to detect the condition of single point cutting tool. First, the vibration signals were collected from the cutting tool and plots were made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the single point cutting tool. In this work, the pretrained networks such as VGG-16, AlexNet, ResNet-50, and GoogLeNet were employed to identify the state of the cutting tool. In the pretrained networks, the effect of hyperparameters such as batch size, solver, learning rate, and train-test split ratio was studied, and the best performing network was suggested for tool condition monitoring.

Misfire Detection in Spark Ignition Engine Using Transfer Learning.

Misfire detection in an internal combustion engine is an important activity. Any undetected misfire can lead to loss of fuel and power in the automobile. As the fuel cost is more, one cannot afford to waste money because of the misfire. Even if one is ready to spend more money on fuel, the power of the engine comes down; thereby, the vehicle performance falls drastically because of the misfire in IC engines. Hence, researchers paid a lot of attention to detect the misfire in IC engines and rectify it. Drawbacks of conventional diagnostic techniques include the requirement of high level of human intelligence and professional expertise in the field, which made the researchers look for intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the misfire in IC engines. This paper proposes the use of transfer learning technology to detect the misfire in the IC engine. First, the vibration signals were collected from the engine head and plots are made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the misfire in the IC engines. In the present work, the pretrained networks such as AlexNet, VGG-16, GoogLeNet, and ResNet-50 are employed to identify the misfire state of the engine. In the pretrained networks, the effect of hyperparameters such as back size, solver, learning rate, and train-test split ratio was studied and the best performing network was suggested for misfire detection.

Authentic Integration of Ethics and AI through Sociotechnical, Problem-Based Learning

Growing awareness of both the demand for artificial intelligence (AI) expertise and of the societal impacts of AI systems has led to calls to integrate learning of ethics alongside learning of technical skills in AI courses and pathways. In this paper, we discuss our experiences developing and piloting the TechHive:AI curriculum for high school youth that integrates AI ethics and technical learning. The design of the curriculum was guided by the following pedagogical goals: (1) to respond to the capacity-building need for critical sociotechnical competencies in AI workforce pathways; and (2) to broaden participation in AI pathways through intentional instructional design to center equity in learning experiences. We provide an overview of the 30-hour learning sequence’s instructional design, and our “4D Framework,” which we use as a heuristic to help students conceptualize and inspect AI systems. We then provide a focused description of one of three 8-hour modules that make up the sequence. Finally, we present evidence of promise from an exploratory study of TechHive:AI with a small sample of students, and discuss insights from implementation, including from our use of established resources for AI learning within the learning sequence as well as those created by our team.

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning

In 2020, the White House released the “Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset,” wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on the graph mining and transformer models. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover ongoing research findings such as the relationship between COVID-19 and oxytocin hormone. All code, details, and pre-trained models are available at https://github.com/IlyaTyagin/AGATHA-C-GP.

Advances in Intelligent Expert Systems for Risk Analysis in a Complex Business Environment with Appropriate Solutions

Today’s commonly used risk management procedures allow the planning team to focus on sensitive areas. It focuses the project team’s attention on activities and resources when a great risk threat exists or when the most time- and effective-engineering solutions can achieve expense reductions. Industrial security issues have worsened dramatically over the last decade. Over time, the risks in the sector rose as limited, single-train or batch operations shifted to massive multi-train operations. Often an accident leads to detrimental effects: shutting down the operation, loss of life, environmental disruption, and loss of business. The reparation of losses from the budget is unwelcome since the government’s reserve will not cope with these needs. This paper has a definitive comparative edge for company sponsors for project risk management (PRM) strategy. The sponsors who knowingly take chances, forecast unfavorable developments, defend themselves against unforeseen incidents and gain experience in price danger take the lead. However, this market benefit is largely dependent on the method to initially identify risks in the architecture of extensive multidisciplinary capital ventures. This paper explores how a sustainable market strategy works in reality. Using the market model, the business issues that need to be considered during the implementation or expansion of urban strategies are discussed. The PRM strategy shows a better-quality assurance ratio of 97.2%, probability ratio of 95.3%, safety rate of 97.5%, reduced risk management rate of 22.6%, an accident rate of 17.4%, cost management of 25.1% and fuel consumption ratio of 23.7% when compared to existing strategies.

Intelligent fault diagnosis expert system for multi-parameter monitor based on fault tree

Aiming at the dilemma of expensive and difficult maintenance, lack of technical data and insufficient maintenance force for modern medical equipment, an intelligent fault diagnosis expert system of multi-parameter monitor based on fault tree was proposed in this study. Firstly, the fault tree of multi-parameter monitor was established and analyzed qualitatively and quantitatively, then based on the analysis results of fault tree, the expert system knowledge base and inference engine were constructed and the overall framework of the system was determined, finally the intelligent fault diagnosis expert system for multi-parameter monitor was developed by using the page hypertext preprocessor (PHP) language, with an accuracy rate of 80% in fault diagnosis. The results showed that technology fusion on the basis of fault tree and expert system can effectively realize intelligent fault diagnosis of multi-parameter monitors and provide troubleshooting suggestions, which can not only provide experience accumulation for fault diagnosis of multi-parameter monitors, but also provide a new idea and technical support for fault diagnosis of medical equipment.

An intelligent expert system for academic advising utilizing fuzzy logic and semantic web technologies for smart cities education

An intelligent expert system for academic advising utilizing fuzzy logic and semantic web technologies for smart cities education

Intelligent Expert System for Diagnosing Faults and Assessing Quality of Power Transformer Insulation Oil by DGA Method

Accurate assessment of oil and fault diagnostics of electrical power transformer insulation for lifelong endurance are the key issues addressed in this research. The durability of a transformer is significantly determined by the quality of its insulation oil, which deteriorates over time due to temperature fluctuations and moisture content. Protecting transformers from potential failure through the early and precise diagnosis of faults and efficient assessment of oil quality during the actual conduct of the operation can avoid sizeable economic losses. The ANFIS Expert System that uses intelligent software plays an important role. The dissolved gas analysis (DGA) in oil is reliable for diagnosing faults and assessing insulation oil quality in transformers. Transformer power plant protection teams often suffer sudden breakdowns that lead to massive damage and huge financial losses. The oil in transformers must be appropriately treated to circumvent such failures. In this research, an ANFIS Expert System was used to diagnose faults and assess insulation oil status and quality in power transformers. by the Rogers ratio method depending on the DGA in oil, a suitable treatment was identified. The graphical user interface from the MATLAB environment was used and proven effective for fault diagnosis and oil quality evaluation. The training algorithm can assess oil quality according to the specifications of the IEEE standard C57-104 and the IEC standard 60599.

Application of Artificial Intelligence System Based on Wireless Sensor Network in Enterprise Management.

With the improvement of the ability to acquire natural information, wireless sensor networks also need to transmit corresponding information in terms of collecting information. Wireless sensor nodes have great application prospects as a key component of wireless sensors. Therefore, different wireless sensors play an important decisive role in the operation of wireless network applications. With the continuous development of wireless sensor networks, existing wireless sensor network nodes exhibit limitations and shortcomings such as inflexible structure, low variability, and low versatility. Specifically, the learning and neural networks obtained by different artificial intelligence expert systems in computing technology are different. On the one hand, it can meet the needs of users for information systems to a certain extent, and on the other hand, it can also help accelerate the development of computer science. At present, the new generation of information technology industry is listed in the seven emerging strategic industries of the country. The new cloud computing technology has gradually expanded to important corporate governance capabilities in terms of information technology. The intelligent application of cloud computing technology replaces traditional enterprise management technology. Efficiency management and risk management can improve the quality and business capabilities of the entire enterprise, improve system applications according to the actual situation of the enterprise, improve system applications, and implement health and the sustainable development of the enterprise, thereby promoting the sustainable development of the computer technology industry.

A committee machine with intelligent experts (CMIE) for estimation of fast and slow shear wave velocities utilizing petrophysical logs

Petrophysical properties determination of a reservoir such as shear wave velocity plays a crucial role in exploration and production management. The purpose of this study is to develop a robust committee machine model for estimating fast and slow shear wave velocities from petrophysical logs. To this end, appropriate inputs were determined and different algorithms including artificial neural network, fuzzy logic and neuro-fuzzy were applied. Afterward, the obtained outputs were merged by utilizing optimization methods consisting of Ant Colony Optimization for Continuous Domain, Genetic and Simulated Annealing approaches. The construction of the committee machine was done using a case study including 2000 data samples from the Sarvak Formation in one of the southwestern Iran oilfields. The utilized dataset was divided into training and testing data which contain 1600 and 400 samples, respectively. Mean Squared Error was presented as a determinative factor for evaluating system performance. To obtain the best system performance with the lowest Mean Squared Error, the parameters setting of each algorithm were coded in different ranges. A total 52416, 76896 and 29400 cases were run to optimize the parameter settings of genetic algorithms, simulated annealing and continues ant colony optimization models, respectively. A weight factor is assigned to each method indicating their contribution in the final estimation. The desirable weights' combination is acquired using optimization methods. Among the individual systems, fuzzy logic and artificial neural network obtained the best performance for estimating fast and slow shear wave velocities, respectively. Also, the results of committee machine algorithms indicate a well optimization on individual systems and have superior performance over them. The most efficient optimization algorithms in the structure of committee machines for fast shear wave velocity are Genetic and Simulated Annealing and for slow shear wave velocity is Genetic Algorithm, in terms of Mean Squared Error criteria.

Using a genetic algorithm to optimize an expert credit rating model

In this article, we show how an “expert” credit rating model can be optimized through the use of a genetic algorithm, a way of combining expert intelligence with artificial intelligence. This innovative method combines transparency and simplicity. It also shows that it is possible to make the model selection and optimisation process less dependent on human judgment alone, without becoming a black box. The model proposed here complies with current (CRR) and future (BCBS, 2017) European banking regulatory requirements. It is also the first study to comply with last November European Banking Authority statement on machine learning for Internal Ratings-Based models (EBA, 2021).

Data-Driven Security and Democratic Intelligence: Key Role of Critical Engagement by Academia

This article raises awareness on the value of epistemic diversity for democratic intelligence governance by highlighting the role of academia. Specifically, it argues that the expertise of critically engaged scholars can play a leading role in challenging the expert discourse of intelligence agencies in the age of Big Data and Artificial Intelligence proliferation. The article examines how the intelligence expert discourse is constructed in the post-Snowden landscape in the United Kingdom by investigating the public communication strategies of Government Communications Headquarters. Driven by a normative rhetoric centered on transparency, the agency’s unprecedented steps toward public engagement are traced through an analysis of key policy documents, official statements, and a museum exhibition. In the ensuing, and novel, context of openness and widespread discursivity around controversial technologies and practices employed by intelligence agencies, critically engaged academia can bring a much-needed diversity of perspective to an increasingly complex and vital policy area in contemporary democratic societies.

Intelligent Data-Driven Decision-Making Method for Dynamic Multisequence: An E-Seq2Seq-Based SCUC Expert System

Under the background of the rapid change of energy technology and the deep integration of artificial intelligence into the power system, it is of great significance to study the intelligent decision-making method of security-constrained unit commitment (SCUC) with high adaptability and high accuracy. Thus, in this article, an expanded sequence-to-sequence (E-Seq2Seq)-based data-driven SCUC expert system for dynamic multiple-sequence mapping samples is proposed. First, dynamic multiple-sequence mapping samples of SCUC are reconstructed by analyzing the input–output sequence characteristics. Then, an E-Seq2Seq approach with a multiple-encoder–decoder architecture and a fully connected extension layer is proposed. On this basis, the simple recurrent unit is introduced as a neuron of the E-Seq2Seq approach to construct deep learning models, and an intelligent data-driven expert system for SCUC is further developed. The proposed approach has been simulated on a typical IEEE 118-bus system and a practical system in Hunan province in China. The results indicate that the proposed approach could possess strong generality, high solution accuracy, and efficiency over traditional methods.

Ian Fleming’s Soviet rival: Roman Kim and Soviet spy fiction during the early cold war

ABSTRACT This article focuses on the life of Roman Kim (1899–1967), an ethnic Korean Soviet counterintelligence officer, and highlights his contribution to the Soviet spy fiction genre during the Cold War. Kim was born in Vladivostok and educated in Japan. He was recruited by the VChK in the early 1920s and was involved in a variety of Soviet counterintelligence operations directed against Japan. Arrested and tortured during the Great Purge, he was the only Soviet Japanese intelligence expert to survive. Kim was released after the end of WWII and reinvented himself as a writer of spy fiction, arguing that the Soviet Union must score victories against the West on the literary front. The article examines Kim’s impact on the literary Cold War by analyzing his most significant spy fiction works, none of which have been translated into English, and chronicles his influence on the later generation of Soviet spy fiction writers, such as Yulian Semyonov and Vasily Ardamatsky, much better known in Russia and in the West.

The importance of transparency in naming conventions, designs, and operations of safety features: from modern ADAS to fully autonomous driving functions

This paper investigates the importance of standardising and maintaining the transparency of advanced driver-assistance systems (ADAS) functions nomenclature, designs, and operations in all categories up until fully autonomous vehicles. The aim of this paper is to reveal the discrepancies in ADAS functions across automakers and discuss the underlying issues and potential solutions. In this pilot study, user manuals of various brands are reviewed systematically and critical analyses of common ADAS functions are conducted. The result shows that terminologies used to describe ADAS functions vary widely across manufacturers and sometimes do not reflect their fundamental functions intuitively. Operational conditions and control procedures also vary across the selected models under this study. Due to this lack of consensus across the industry, drivers are not aware or well informed about ADAS functions in their vehicles, leading to a very low utilization rate and may lead to misuse of those functions. This paper provides insightful suggestions for the transport industry, Artificial Intelligence (AI) experts, and regulators to design frameworks and guidelines in governing the naming convention, operating conditions, control procedures, and information disclosure of ADAS. Such guidelines can be the foundations for regulating future AI-based self-driving functions.

AKER: An open-source security platform integrating IDS and SIEM functions with encrypted traffic analytic capability

ABSTRACT This research proposes Aker, an open-source security platform that integrates IDS and SIEM functions while supporting the automated investigation of threats hidden in encrypted traffic. Aker is based on open-source technologies, whose aim is to simplify and integrate network monitoring and security alerts management through coherent and interconnected dashboards. Aker enables the detection of malware concealed in encrypted traffic and uses a heuristic-based decision-tree model to classify these threats into different severity levels, based on seven specific criteria and indicators originating mainly from TLS handshake unencrypted metadata, and HTTP/ DNS contextual flows linked to the encrypted traffic. Our approach does not require deep packet inspection nor intensive traffic analysis, but rather relies on the analysis of a reduced set of network telemetry indicators and session descriptors. Aker permits to automate data collection and investigation while providing user-friendly dedicated dashboards to provide SOC analysts and threat intelligence experts with valuable decision support tools. We present the design and implementation aspects of the proposed platform along with some validation results.

Intelligence and scientific expertise

In this article we develop a pragmatist-inspired notion of intelligence that should lead to a better understanding of the notion of scientific expertise. The notion of intelligence is drawn from Dewey and is therefore used here in its technical sense. Our thesis is that scientific knowledge is a necessary but not sufficient condition for scientific expertise; intelligence should also be added. Conceived of as the capacity to apply general knowledge to particulars, we reach the conclusion that intelligence is a necessary requirement for scientific experts in the wake of Dewey’s logic of inquiry. In particular, we argue that an all-important task that scientific experts are asked to accomplish, and which puts their expertise to the test, is to transform indeterminate situations into problematic situations, and that such a goal can only be achieved if scientific experts succeed in paying attention to all the contingent and precarious aspects that make the situation they face unique.

The development of artificial intelligence in the digital economy

In the article, the author highlights the development of the country's digital economy based on the use of artificial intelligence (AI), general artificial intelligence. The growth potential of the high-tech sector in the context of economic sanctions is shown. An analysis of the modern legal framework is given, which determines the strategy for the implementation of AI, and the economic essence of the conceptual apparatus that describes various types of artificial intelligence is revealed. The variants of practical application of artificial intelligence expert systems in Russia are considered.

Membangun Wirausaha dan Kemandirian Anak-Anak Panti Asuhan Al Ihsan Melalui Pelatihan Pembuatan Selai Tomat

The ideal entrepreneur figure requires human qualities that can be competitive. Upgrading humans into functional beings is insufficient to provide intelligence, skills or technical expertise. The primary priority needed is to build a good mental attitude. The training activity for making tomato jam is an opportunity for the orphanage children to increase their knowledge with the hope that their abilities and knowledge will develop and be applied in their lives. The motivation given to the training activities made the children feel that being a child in an orphanage was not an obstacle to continuing to develop. This service activity aims to educate orphanage children with training in making tomato jam as a provision for entrepreneurship. This community service activity was carried out in two stages, including lectures for delivering material and demonstrations of making tomato jam by the service team for orphanage children. The participants of this activity were 25 girls from the Al Ihsan Orphanage in Padang, West Sumatra. Based on the results of the questionnaire that the training participants filled out after the activity, showed that the orphanage children understood every process and practice in making tomato jam well.

Predicting Patient-Level 3-Level Version of EQ-5D Index Scores From a Large International Database Using Machine Learning and Regression Methods

ObjectivesThis study aimed to evaluate the performance of machine learning and regression methods in the prediction of 3-level version of EQ-5D (EQ-5D-3L) index scores from a large diverse data set. MethodsA total of 30 studies from 3 countries were combined. Predictions were performed via eXtreme Gradient Boosting classification (XGBC), eXtreme Gradient Boosting regression (XGBR) and ordinary least squares (OLS) regression using 10-fold cross-validation and 80%/20% partition for training and testing. We evaluated 6 prediction scenarios using 3 samples (general population, patients, total) and 2 predictor sets: demographic and disease-related variables with/without patient-reported outcomes. Model performance was evaluated by mean absolute error and percent of predictions within clinically irrelevant error range and within correct health severity group (EQ-5D-3L index <0.45, 0.45-0.926, >0.926). ResultsThe data set involved 26 318 individuals (clinical settings n = 6214, general population n = 20 104) and 26 predictor variables plus diagnoses. Using all predictors and the total sample, mean absolute error values were 0.153, 0.126, and 0.131, percent of predictions within clinically irrelevant error range were 47.6%, 39.5%, and 37.4%, and within the correct health severity group were 56.3%, 64.9%, and 63.3% by XGBC, XGBR, and OLS, respectively. The performance of models depended on the applied evaluation criteria, the target population, the included predictors, and the EQ-5D-3L index score range. ConclusionsRegression models (XGBR and OLS) outperformed XGBC, yet prediction errors were outside the clinically irrelevant error range for most respondents. Our results highlight the importance of systematic patient-reported outcome (EQ-5D) data collection. Dialogs between artificial intelligence and outcomes research experts are encouraged to enhance the value of accumulating data in health systems.