Expert Module Research Articles

HeMTAN: Hybrid task-adapted experts-based multi-task attention network for unseen compound fault decoupling diagnosis of rotating machinery

In a rotating machinery system, a single fault of one component often causes damage to other related components, thus inducing compound faults. Without compound fault data to train intelligent models, the realization of decoupling diagnosis and accurate identification of unseen compound faults is not only of great practical significance for the safety management of equipment operation and maintenance, but also remains a challenging topic. Considering some shortcomings in the current intelligent diagnosis of compound faults, as well as the relatedness and difference between different fault features in compound fault signals, a hybrid task-adapted experts-based multi-task attention network (HeMTAN) model is investigated in this paper, which can be used for identify single faults and unseen compound faults in mechanical transmission systems. Firstly, variational mode decomposition is combined with Hilbert-Huang transform to obtain time–frequency graphs of time series signal as model input, so as to better characterize different fault features. Secondly, a hybrid task-adapted expert module is designed to extract the common and some private feature information of different learning tasks from different multi-perspective, and then the important information related to the specific learning task is further mined by the constructed private feature attention-based densely connected module. Finally, the diagnosis results can be obtained by fusing the outputs of the classifier of the two learning tasks. The performance of the investigated HeMTAN model is analyzed by the gearbox compound fault dataset and rolling bearing compound fault dataset, and the results demonstrate that the investigated HEMTAN method has significantly improved diagnosis accuracy and generalization performance.

A Novel Energy Based Model Mechanism for Multi-Modal Aspect-Based Sentiment Analysis

Multi-modal aspect-based sentiment analysis (MABSA) has recently attracted increasing attention. The span-based extraction methods, such as FSUIE, demonstrate strong performance in sentiment analysis due to their joint modeling of input sequences and target labels. However, previous methods still have certain limitations: (i) They ignore the difference in the focus of visual information between different analysis targets (aspect or sentiment). (ii) Combining features from uni-modal encoders directly may not be sufficient to eliminate the modal gap and can cause difficulties in capturing the image-text pairwise relevance. (iii) Existing span-based methods for MABSA ignore the pairwise relevance of target span boundaries. To tackle these limitations, we propose a novel framework called DQPSA. Specifically, our model contains a Prompt as Dual Query (PDQ) module that uses the prompt as both a visual query and a language query to extract prompt-aware visual information and strengthen the pairwise relevance between visual information and the analysis target. Additionally, we introduce an Energy-based Pairwise Expert (EPE) module that models the boundaries pairing of the analysis target from the perspective of an Energy-based Model. This expert predicts aspect or sentiment span based on pairwise stability. Experiments on three widely used benchmarks demonstrate that DQPSA outperforms previous approaches and achieves a new state-of-the-art performance. The code will be released at https://github.com/pengts/DQPSA.

Frequency-Adaptive Pan-Sharpening with Mixture of Experts

Pan-sharpening involves reconstructing missing high-frequency information in multi-spectral images with low spatial resolution, using a higher-resolution panchromatic image as guidance. Although the inborn connection with frequency domain, existing pan-sharpening research has not almost investigated the potential solution upon frequency domain. To this end, we propose a novel Frequency Adaptive Mixture of Experts (FAME) learning framework for pan-sharpening, which consists of three key components: the Adaptive Frequency Separation Prediction Module, the Sub-Frequency Learning Expert Module, and the Expert Mixture Module. In detail, the first leverages the discrete cosine transform to perform frequency separation by predicting the frequency mask. On the basis of generated mask, the second with low-frequency MOE and high-frequency MOE takes account for enabling the effective low-frequency and high-frequency information reconstruction. Followed by, the final fusion module dynamically weights high frequency and low-frequency MOE knowledge to adapt to remote sensing images with significant content variations. Quantitative and qualitative experiments over multiple datasets demonstrate that our method performs the best against other state-of-the-art ones and comprises a strong generalization ability for real-world scenes. Code will be made publicly at https://github.com/alexhe101/FAME-Net.

Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations

Accurate prediction of coupon usage is crucial for promoting user consumption through targeted coupon recommendations. However, in real-world coupon recommendations, the coupon allocation process is not solely determined by the model trained with the history interaction data but is also interfered with by marketing tactics desired to fulfill specific commercial goals.This interference creates an imbalance in the interactions, which causes the data to deviate from the user's natural preferences. We refer to this deviation as the matching bias. Such biased interaction data affects the efficacy of the model, and thus it is necessary to employ debiasing techniques to prevent any negative impact. We investigate the mitigation of matching bias in coupon recommendations from a causal-effect perspective. By treating the attributes of users and coupons associated with marketing tactics as confounders, we find the confounders open the backdoor path between user-coupon matching and the conversion, which introduces spurious correlation. To remove the bad effect, we propose a novel training paradigm named Backdoor Adjustment via Group Adaptation (BAGA) for debiased coupon recommendations, which performs intervened training and inference, i.e., separately modeling each user-coupon group pair. However, modeling all possible group pairs greatly increases the computational complexity and cost. To address the efficiency challenge, we further present a simple but effective dual-tower multi-task framework and leverage the Customized Gate Control (CGC) model architecture, which separately models each user and coupon group with a separate expert module. We instantiate BAGA on five representative models: FM, DNN, NCF, MASKNET, and DEEPFM, and conduct comprehensive offline and online experiments to demonstrate the efficacy of our proposed paradigm.

SkillCLIP: Skill Aware Modality Fusion Visual Question Answering (Student Abstract)

When humans are posed with a difficult problem, they often approach it by identifying key skills, honing them, and finally effectively combining them. We propose a novel method and apply it for the VizWiz VQA task to predict the visual skills needed to answer a question, and leverage expert modules to produce intermediary outputs and fuse them in a skill-aware manner. Unlike prior works in visual question-answering (VQA) that use intermediate outputs such as detected objects and Optical Character Recognition (OCR), our approach explicitly guides the model with a skill embedding on what to focus on. While our results show that using skill-aware fusion outperforms skill-unaware models for only a subset of questions, we believe our results provide interesting directions for future work. We also release our code, model, and illustrative demonstrations for future research purposes.

Legal Judgment Prediction via graph boosting with constraints

Legal Judgment Prediction (LJP) is a multi-task multi-label problem in the civil law system, involving the prediction of law articles, charges, and terms of penalty based on fact descriptions. However, most existing research approaches LJP as a single-label scenario, neglecting the correlations between multiple labels and failing to consider cross-task consistency constraints in a multi-label scenario. Moreover, although previous multi-task studies have proposed expert models and coarse-grained topology construction for inter-task relationships, the former neglects rich information exchange among different tasks, and the latter, if one task’s prediction is inaccurate, will affect subsequent tasks. This paper has designed legal label graphs and proposed a novel graph boosting with constraints framework, GJudge, for legal judgment prediction to address these limitations. The framework comprises a multi-perspective interactive encoder and a multi-graph attention consistency expert module. The encoder utilizes bidirectional LSTM, gated attention units, cross attention, and graph attention networks to integrate fact descriptions and label similarity relationships information from legal label graphs for multi-perspective interactive encoding. The expert module utilizes the multiple expert networks and the multi-graph attention network to differentiate between confusing labels and ensure consistent constraints across tasks, this is achieved through the fusion of label consistency constraints and confusion relationships information in the legal label graphs. Experimental results on two real-world datasets across different tasks show an improvement in F1 scores ranging from at least 0.93% to a maximum of 2.97%, illustrating the effectiveness of GJudge compared to the state-of-the-art model.

Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents.

Accurately and rapidly acquiring the microscopic properties of a material is crucial for catalysis and electrochemistry. Characterization tools, such as spectroscopy, can be a valuable tool to infer these properties, and when combined with machine learning tools, they can theoretically achieve fast and accurate prediction results. However, on the path to practical applications, training a reliable machine learning model is faced with the challenge of uneven data distribution in a vast array of non-negligible solvent types. Herein, we employ a combination of the first-principles-based approach and data-driven model. Specifically, we utilize density functional theory (DFT) to calculate theoretical spectral data of CO-Ag adsorption in 23 different solvent systems as a data source. Subsequently, we propose a hierarchical knowledge extraction multiexpert neural network (HMNN) to bridge the knowledge gaps among different solvent systems. HMNN undergoes two training tiers: in tier I, it learns fundamental quantitative spectra-property relationships (QSPRs), and in tier II, it inherits the fundamental QSPR knowledge from previous steps through a dynamic integration of expert modules and subsequently captures the solvent differences. The results demonstrate HMNN's superiority in estimating a range of molecular adsorbate properties, with an error range of less than 0.008 eV for zero-shot predictions on unseen solvents. The findings underscore the usability, reliability, and convenience of HMNN and could pave the way for real-time access to microscopic properties by exploiting QSPR.

Deep reinforcement learning for fuel cost optimization in district heating

This study delves into the application of deep reinforcement learning (DRL) frameworks for optimizing setpoints in district heating systems, which experience hourly fluctuations in air temperature, customer demand, and fuel prices. The potential for energy conservation and cost reduction through setpoint optimization, involving adjustments to supply temperature and thermal energy storage utilization, is significant. However, the inherent nonlinear complexities of the system render conventional manual methods ineffective. To address these challenges, we introduce a novel learning framework with an expert knowledge module tailored for DRL techniques. The framework leverages system status information to facilitate learning. The training is performed by employing model-free DRL methods and a refined digital twin of the Espoo district heating system. The expert module, accounting for power plant capacities, ensures actionable directives aligned with operational feasibility. Empirical validation through comprehensive simulations demonstrates the efficacy of the proposed approach. Comparative analyses against manual methods and evolutionary techniques highlight the approach's superior ability to curtail fuel costs. This study advances the understanding of DRL in district heating optimization, offering a promising avenue for enhanced energy efficiency and cost savings.

Achieving Human Parity on Visual Question Answering

The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topic with an increasing number of real-world applications in the last decade. This paper introduces a novel hierarchical integration of vision and language AliceMind-MMU (ALIbaba’s Collection of Encoder-decoders from Machine IntelligeNce lab of Damo academy - MultiMedia Understanding) , which leads to similar or even slightly better results than a human being does on VQA. A hierarchical framework is designed to tackle the practical problems of VQA in a cascade manner including: (1) diverse visual semantics learning for comprehensive image content understanding; (2) enhanced multi-modal pre-training with modality adaptive attention; and (3) a knowledge-guided model integration with three specialized expert modules for the complex VQA task. Treating different types of visual questions with corresponding expertise needed plays an important role in boosting the performance of our VQA architecture up to the human level. An extensive set of experiments and analysis are conducted to demonstrate the effectiveness of the new research work.

Addressing Confounding Feature Issue for Causal Recommendation

In recommender systems, some features directly affect whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, short videos are objectively easier to finish even though the user may not like the video. We term such feature as confounding feature , and video length is a confounding feature in video recommendation. If we fit a model on such interaction data, just as done by most data-driven recommender systems, the model will be biased to recommend short videos more, and deviate from user actual requirement. This work formulates and addresses the problem from the causal perspective. Assuming there are some factors affecting both the confounding feature and other item features, e.g., the video creator, we find the confounding feature opens a backdoor path behind user-item matching and introduces spurious correlation. To remove the effect of backdoor path, we propose a framework named Deconfounding Causal Recommendation (DCR) , which performs intervened inference with do-calculus . Nevertheless, evaluating do-calculus requires to sum over the prediction on all possible values of confounding feature, significantly increasing the time cost. To address the efficiency challenge, we further propose a mixture-of-experts (MoE) model architecture, modeling each value of confounding feature with a separate expert module. Through this way, we retain the model expressiveness with few additional costs. We demonstrate DCR on the backbone model of neural factorization machine (NFM) , showing that DCR leads to more accurate prediction of user preference with small inference time cost. We release our code at: https://github.com/zyang1580/DCR .

DEVELOPMENT OF SCIENCE MODULE BASED ON INQUIRY SCIENCE ISSUES TO IMPROVE STUDENTS' CRITICAL THINKING SKILLS

This study aims to 1) know how to develop modules, 2) know the feasibility of the module, and 3) know the effectiveness of the IPA module with an approach of Inquiry Science Issues on Climate Change Materials to improve the critical thinking skills of students in grade VII junior high school. The development model used in this study is ADDIE (Analysis, Design, Development, Implementation, and Evaluation). Data collection techniques using observation methods, interviews, as well as documentation. Quantitative analysis techniques for analyzing data have been validated by experts and module assessments by practitioners and students. In contrast, the analysis of qualitative descriptive data to analyze the resulting module data. The results of the study concluded that 1) Modules developed based on the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation) with inquiry science issues that use critical thinking indicators. 2) Module eligibility in terms of validation results Material Expert Module got the same result of 90.27% with categories Excellent, media expert validation of 89.06% and 88.43% respectively with excellent category, practitioner ratings of 88.33% and 87.91% respectively with excellent categories, student ratings of 81.05% with very categories good. 3) The effectiveness of the module analyzed using the N-gain test obtained a result of 0.5014; 0.3 g ≤ ≤ 0.7, so the module is said to be effective with a moderate category.

Monitoring the green evolution of vernacular buildings based on deep learning and multi-temporal remote sensing images

The increasingly mature computer vision (CV) technology represented by convolutional neural networks (CNN) and available high-resolution remote sensing images (HR-RSIs) provide opportunities to accurately measure the evolution of natural and artificial environments on Earth at a large scale. Based on the advanced CNN method high-resolution net (HRNet) and multi-temporal HR-RSIs, a framework is proposed for monitoring a green evolution of courtyard buildings characterized by their courtyards being roofed (CBR). The proposed framework consists of an expert module focusing on scenes analysis, a CV module for automatic detection, an evaluation module containing thresholds, and an output module for data analysis. Based on this, the changes in the adoption of different CBR technologies (CBRTs), including light-translucent CBRTs (LT-CBRTs) and non-light-translucent CBRTs (NLT-CBRTs), in 24 villages in southern Hebei were identified from 2007 to 2021. The evolution of CBRTs was featured as an inverse S-curve, and differences were found in their evolution stage, adoption ratio, and development speed for different villages. LT-CBRTs are the dominant type but are being replaced and surpassed by NLT-CBRTs in some villages, characterizing different preferences for the technology type of villages. The proposed research framework provides a reference for the evolution monitoring of vernacular buildings, and the identified evolution laws enable to trace and predict the adoption of different CBRTs in a particular village. This work lays a foundation for future exploration of the occurrence and development mechanism of the CBR phenomenon and provides an important reference for the optimization and promotion of CBRTs.

Development and Validation of an Automatic System for Intracerebral Hemorrhage Medical Text Recognition and Treatment Plan Output.

The main purpose of the study was to explore a reliable way to automatically handle emergency cases, such as intracerebral hemorrhage (ICH). Therefore, an artificial intelligence (AI) system, named, H-system, was designed to automatically recognize medical text data of ICH patients and output the treatment plan. Furthermore, the efficiency and reliability of the H-system were tested and analyzed. The H-system, which is mainly based on a pretrained language model Bidirectional Encoder Representations from Transformers (BERT) and an expert module for logical judgment of extracted entities, was designed and founded by the neurosurgeon and AI experts together. All emergency medical text data were from the neurosurgery emergency electronic medical record database (N-eEMRD) of the First Affiliated Hospital of Chongqing Medical University, Chongqing Emergency Medical Center, and Chongqing First People’s Hospital, and the treatment plans of these ICH cases were divided into two types. A total of 1,000 simulated ICH cases were randomly selected as training and validation sets. After training and validating on simulated cases, real cases from three medical centers were provided to test the efficiency of the H-system. Doctors with 1 and 5 years of working experience in neurosurgery (Doctor-1Y and Doctor-5Y) were included to compare with H-system. Furthermore, the data of the H-system, for instance, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristics curve (AUC), were calculated and compared with Doctor-1Y and Doctor-5Y. In the testing set, the time H-system spent on ICH cases was significantly shorter than that of doctors with Doctor-1Y and Doctor-5Y. In the testing set, the accuracy of the H-system’s treatment plan was 88.55 (88.16–88.94)%, the specificity was 85.71 (84.99–86.43)%, and the sensitivity was 91.83 (91.01–92.65)%. The AUC value of the H-system in the testing set was 0.887 (0.884–0.891). Furthermore, the time H-system spent on ICH cases was significantly shorter than that of doctors with Doctor-1Y and Doctor-5Y. The accuracy and AUC of the H-system were significantly higher than that of Doctor-1Y. In addition, the accuracy of the H-system was more closed to that of Doctor-5Y. The H-system designed in the study can automatically recognize and analyze medical text data of patients with ICH and rapidly output accurate treatment plans with high efficiency. It may provide a reliable and novel way to automatically and rapidly handle emergency cases, such as ICH.

L'intelligence artificielle au service des départements d'information médicale : construction et évaluation d'un outil d'aide à la décision pour cibler et prioriser les séjours à contrôler et fiabiliser les recettes hospitalières générées par la tarification à l'activité

BackgroundMedical Information Departments help to optimize the hospital revenues generated by activity-based pricing. A review of medical files, selected after the targeting of coding summaries, is organized. The aim is to make any corrections to the diagnoses or coded procedures with a potential impact on the pricing of the stay. Targeting is of major importance as a means of concentrating resources on the files for which coding can be effectively improved. The tools available for targeting can be optimized. We have developed a decision-making support tool to make targeting more efficient. The objective of our study was to evaluate the performance of this tool. MethodsThe tool combines an artificial intelligence module with a rule-based expert module. A predictive score is assigned to each coding summary that reflects the probability of a revalued stay. Evaluation of the performance of this tool was based on a sample of 400 stays of at least 3 nights of patients hospitalized at the Paris Saint-Joseph Hospital from 1st November to 31st December 2019. Each stay was reviewed by a coding expert, without knowledge of the score assigned and without help from expert queries. Two main assessment criteria were used: area under the ROC curve and positive predictive value (PPV). ResultsThe area under the ROC curve was 0.70 (CI 95% [0.64-0.76]). With a revalued coding rate of 32%, PPV was 41% for scores above 5, 65% for scores above 8, 88% for scores above 9. ConclusionThe study made it possible to validate the performance of the tool. The implementation of new variables could further increase its performance. This is an area of development to be considered, particularly with in view of generalizing individual invoicing in hospitals.

Rule-based expert system supporting Individual Education-and-Therapeutic Program composition in SYSABA

System for Applied Behavioral Analysis (SYSABA) is a freeware web-based software developed in Poland that aids therapists who work with developmentally disordered children. A pioneer in Poland application is adapted to Applied Behavior Analysis (ABA) methodology and is compliant with the Polish Regulation of the Ministry of Education. A part of SYSABA is a module that helps compose a compliant with Polish regulations and ABA methodology, the Individual Education-and-Therapeutic Program (pol. IPET). This development process is guided by an expert system, including specialists’ knowledge of the subject. The IPET composition is demanding and time-consuming, and the decisions made by the therapist will influence the treatment results. Because there are many paths to the final curriculum, the decision-making module in SYSABA was designed to improve and standardize the process.The paper provides details of the IPETs expert module design in SYSABA. The expert knowledge of long-term supervisors was expressed in a set of regulations and constraints supplying a rule-based expert system. The unstructured expertise was translated into a logical SYSABA two-level framework. Thirty-six rules guiding skill and behavior curricula were created. The effectiveness of the expert system was confirmed by a survey after one year of SYSABA usage.

Is present pharmacy education adapted to needs? Survey results from young practitioner views regarding pharmacy education outcome towards a national reform in Hungary

BackgroundThe pharmacist career is constantly adapting to societal and health care needs. The past decade has seen a growing demand for curricular development to align graduation outcome with workforce competencies. ObjectiveThis study aims to identify expectations for both didactic and experiential components of a new curriculum based on young pharmacist practitioner views. MethodsAn online survey questionnaire was used in 2019–2020 to evaluate the pharmacy curriculum to detect indicators or key areas which require comprehensive reform. ResultsThe predominant majority of the 205 study participants recommended reduction in credit hours for Natural Sciences (78.54%) and a similar increase in the Theoretical and Practical Expertise Module (77.9%). Pharmaceutical care, clinical therapeutics and clinical pharmacy competencies should also be more highlighted in the program. Findings indicate the current training does not prepare for problem-solving and daily workplace challenges (72.7%) or for extended pharmacist skills and competencies (71.71%). Results show inconsistency in practical training experience, as all respondents participated in practical training for drug manufacturing and analysis but 61.0% reported no hands-on skills training in a hospital-clinical simulation setting. Indications for practitioner involvement into the natural sciences and biomedical subjects (86.3%) confirm the obvious need for more practice-oriented education. ConclusionsEducational reforms seem to be inevitable to achieve measurable improvement in professional practice and skills competency. The country specific demand for a needs-based pharmacy education reflects global trends but may also provide useful insights for individual transitions to transform education through practice and improve practice through education.

Climate Risk with Particular Emphasis on the Relationship with Credit-Risk Assessment: What We Learn from Poland

This research seeks to identify non-financial enterprises exposed to the climate risk relating to transition risks and at the same time use of bank loans, as well as to conduct stress tests to take account of the financial risk related to climate change. The workflow through which to determine the ability of the banking sector to assess the potential impact of climate risk entails parts based around economic sector and company level. The procedure based on the sectoral level identifies vulnerable economic sectors (in the Sectoral Module), while the procedure based on company level (the Company Module) refers to scenarios presented in stress tests to estimate the probability of default under stressful conditions related to the introduction of a direct carbon tax. The introduction of the average direct carbon tax (EUR 75/tCO2) in fact results in increased expenditure and reduced sales revenues among enterprises from sectors with a high CO2 impact, with the result being a decrease in the profitability of enterprises, along with a simultaneously higher level of debt; an increase in the probability of default (PD) from 3.6%, at the end of 2020 in the baseline macroeconomic scenario, to between 6.31% and 10.12%; and increased commercial bank capital requirements. Financial institutions should thus use PD under stressful conditions relating to climate risk as suggestions to downgrade under the expert module.

The Development of Online Mathematic Module Based on Dick and Carey Theory for Junior High School Student

This study aims to develop an online mathematic module using Dick and Carey's development model. This research was conducted at SMP Al-Kahfi Sepulu Bangkalan Regency. The validation and quality assessment was conducted by questionnaire, namely questionnaires of expert modules, design experts, and teachers' colleagues. The trials were carried out by students in small groups and large groups. The researchers first tried it in a small group (5 students) before implementing it to a large group of students. The results of this research are obtained from the subject matter expert assessment, peer teacher's questionnaire, and field tests for small and large groups of students. These processes are required to fulfill the validation requirements so that the learning media produced is of high quality. The researcher concluded that the online learning module was feasible. Therefore, this product could be distributed to teachers and students for a better learning experience.

The software part, the fundamental and organizational structure of the software and hardware complex to ensure the safety of hydraulic and hydropower structures under the construction

Relevance. To ensure the safety of hydraulic and hydropower facilities, to organize constant monitoring of their condition, including the installation of additional control and measuring equipment is required. It is also necessary to introduce modern information and diagnostic systems that allow in real time to assess the state of both individual elements of the structure, as well as mutually influencing structures and complexes of structures. At the same time, the results presented in the article are aimed for describing the fundamental structure a new generation of information and diagnostic system building, developed within the framework of a software and hardware complex. The aims of the work. The main purpose of the proposed system is to increase the level of monitoring to ensure the safety of hydraulic structures, which have a possible impact on each other during operation and construction. The tasks of creating a unified platform for assessing the safe state of hydraulic structures during the construction and operation of hydraulic structures (hydroelectric and pumped-storage power plants) are being solved, the foundations of a unified information-diagnostic system on a modular basis are laid, which allows comparing monitoring and measuring equipment readings, statistical samples and calculated values obtained within the mathematical modeling objects. Methods. The paper presents the structure of the interaction of individual blocks included in the information-diagnostic system, the interaction of the information-diagnostic system with the user for decision-making during the operation of the hydraulic structures. Results. A modular shell has been implemented, consisting of a combined information-diagnostic system, expert and calculation modules, which allows a comprehensive approach to the issue of safety of mutually affecting hydraulic structures. Software links have been developed to assess the change in parameters that can lead to deviations/irregularities in the operation of the hydraulic structures.

Technology for developing expert modules and DSS comprehensive platform for reliability and risks monitoring in the Oil and Gas Engeneering

The paper describes the proposed technology for the development of expert modules and DSS for integrated reliability and risk control systems for oil and gas industry, as part of a complex of intelligent technological management of reliability and efficiency of oil and gas systems developed at TIU. All stages of the development of system modules for controlling technical risks, the features of the structure of the decision-making model for DSS, and the components of a static and dynamic expert system for transport and storage of hydrocarbons are considered. The features of creating decision support systems are described, their main functions and decision criteria and their main provisions. As an example, one of the modules of intelligent neural network engineering control for predicting halos of thawing soil in the vicinity of the pipeline and passing communications, and buildings is shown, a model for visualizing processing results is proposed.