Sequence Of Actions Research Articles

A closed-loop human-computer interactive design method based on sequential human intention prediction and knowledge recommendation

Designers working on large-scale complex engineering projects may encounter design errors due to limited experience and expertise. Therefore, during the design process, accurately identifying the designer’s design intention and recommending relevant design knowledge can enhance design efficiency and reduce errors. In this paper, we propose a closed-loop human–computer interactive design method based on sequential human intention prediction and knowledge recommendation. This method leverages the Function-Behaviour-Structure (FBS) model to reduce the dimensionality of design action sequences, so as to facilitate the analysis of potential design patterns. The processed action sequences are used to train Transformer to predict design intentions. In different input sequences, Transformer achieved a highest prediction accuracy of 92.09%. We construct a Design Knowledge Recommendation Framework (DKRF) and its corresponding design knowledge matching algorithm. This framework accurately recommends design knowledge to designers, solving design stagnation and improving design efficiency. Finally, a case study of four types of mechanical model design is conducted to demonstrate the feasibility and wide applicability of the proposed closed-loop human–computer interactive design method.

Ecosystem Approach in Agrivoltaic Parks Design

The approach to strategic landscape design faces today two challenges: a) the reduction of biodiversity loss b) the mitigation of the negative impact that anthropic activities have on ecosystems. As these are subject to a continuous transformation process, it is essential that the design methodology considers its underlying properties. Hence, arises the need of a profound paradigm shift: integrate better the human landscape into the natural one. To achieve this goal, it is necessary to understand first landscape dynamics as well as the mechanisms that facilitate its resilience and functionality in ecological terms. This is possible thanks to the strategic framework of the Ecological Infrastructure. The landscape proposal must evaluate then the sequence of actions to be carried out, the measures necessary for the improvement of the recognised systems, and its possible evolution over time. Then, according to the Sustainable Development Goals (SDGs), agreed by the EU 2030 biodiversity strategies, the proposal must demonstrate: How biodiversity will improve and how it will be preserved - How ecological processes linked to biodiversity will be upgraded - How cultural and social processes linked to the site will be enhanced - How agricultural and energy production will increase land efficiency - How disturbances will be modulated and controlled. This methodology carried out in Spain aims to demonstrate that the implementation of agrivoltaic fields create an optimal synergy to ensure food security and mitigate the effects of climate change, but also provide the chance to make the landscape more sustainable, efficient, and beautiful.

A brain machine interface framework for exploring proactive control of smart environments

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.

Analisis Perkembangan Kognitif Pada Anak Autis Di Flexi School Banda Aceh

There are many challenges and problems faced by autistic children regarding cognitive development. Occupational therapy is a treatment that aims to help someone who has physical, mental and cognitive limitations. This therapy is carried out so that sufferers can become less dependent on other people to live their daily lives. So they can maximize their cognitive potential. The aim of this research is to obtain information about cognitive development in autistic children in flexi schools and how to handle it. The method used in this research is descriptive qualitative research. The subject of this research is a student at a flexi school and the object of this research is occupational therapy to improve cognitive development in autistic children. Data analysis techniques are carried out by reducing data, presenting data and drawing conclusions. The data collection techniques used were field observation, interviews and documentation. The conclusion from the results of this research is that children experience an increase in cognitive development, namely they are able to understand the meaning of simple commands, the order and grouping of objects, and the sequence of actions. However, they still need repetition to strengthen their understanding

Teaching Artificial Intelligence Good Air Traffic Flow Management

Air traffic flow managers are continually faced with the decision of when and how to respond to predictions of future constraints. The promise of artificial intelligence, and specifically reinforcement learning, to provide decision support in this domain stems from the ability to systematically evaluate a sequence of potential actions, or strategy, across a range of uncertain futures. As decision support for human traffic managers, the generated recommendations must embody characteristics of a good management strategy; doing so requires introducing such notions to the algorithm. This paper proposes inducing stability into the strategy by dynamically constraining the design space based on upstream design decisions to promote consistency in the recommendations over time, where two such constraint sets are considered. The paper further evaluates the impact of adding a performance improvement threshold that must be overcome to accept a new strategy recommendation. The combination of search constraints and threshold values is evaluated against the agent’s reward function in addition to measures proposed to capture the stability of the strategy. The results show that the more restrictive set of constraints yields the best performance in terms of strategy stability and is more likely to reduce the delay where implementation of the threshold has a minor impact on overall performance. However, for the highest impact day of 8 June 2018, applying the threshold reverses the performance gains in delay but dramatically improves the stability of the resulting traffic flow management strategy from a flight level perspective, implying a potential tradeoff between delay optimization and flight predictability.

Decomposing Unit for Agricultural Waste

The decomposer unit is an essential part of the process that turns organic waste and raw materials into fertilizers, which makes a big difference in waste management and sustainable farming methods. This unit takes raw materials, mostly organic waste, and starts a sequence of actions to turn them into fertilizer that can be used. Organic matter breaks down chemically during decomposition, producing simpler chemicals that are high in potassium, phosphate, and nitrogen—three important nutrients. For the purpose of increasing soil fertility and fostering plant growth, these nutrients are essential. This process is optimized by the decomposer unit, which guarantees effective raw material usage and reduces waste

Which Actions Are Always Necessary in Fully-Observable Non-Deterministic Planning?

Explaining the sequential action choices of an autonomous agent is essential for understanding the agent and learning from the agent. Given a goal-reaching action sequence in a non-deterministic environment, one important aspect of explaining action choices is deciding if the action choice is always necessary for the action sequence, or if it may be omitted under certain circumstances. Most works in explainable planning (XAIP) have been focused on explanation of action choices in deterministic environments as assumed by classical planning, and one widely used notion is causal link. In this work, we will apply the notion of causal link to justifying actions choices of a trace in fully-observable non-determinisitic (FOND) settings. We will also introduce a new notion -- the always-necessary actions of a trace -- and propose an approach that derives the always-necessary actions of a trace from the causal link justifications of action choices.

It’s not distance but similarity of distance: changing stimulus relations affect the control of action sequences

Interacting with our environment happens on different levels of complexity: While there are individual and simple actions like an isolated button press, most actions are more complex and involve sequences of simpler actions. The degree to which multiple simple actions are represented as one action sequence can be measured via so-called response-response binding effects. When two or more responses are executed consecutively, they are integrated into one representation so that repetition of one response can start retrieval of the other. Executing such an action sequence typically involves responding to multiple objects or stimuli. Here, we investigated whether the spatial relation of these stimuli affects action sequence execution. To that end, we varied the distance between stimuli in a response-response binding task. Stimulus distance might affect response-response binding effects in one of two ways: It might directly affect the representation of the response sequence, making integration and retrieval between responses more likely if the responses relate to close stimuli. Alternatively, the similarity of stimulus distribution during integration and retrieval might be decisive, leading to larger binding effects if stimulus distance is identical during integration and retrieval. We found stronger binding effects with constant than with changing stimulus distance, indicating that action integration and retrieval can easily affect performance also if responses refer to separated objects. However, this effect on performance is diminished by changing spatial distribution of stimuli at the times of integration and retrieval.

МОДЕЛЬ ОБРАЩЕНИЯ ЛЕКАРСТВЕННЫХ ПРЕПАРАТОВ НА БАЗЕ ОТЕЧЕСТВЕННОЙ АНАЛИТИЧЕСКОЙ ПЛАТФОРМЫ LOGINOM

Striving to improve the economic efficiency of pharmaceutical business in an unstable market situation and high competition requires the subjects of drug circulation to implement modern approaches to the organization of their activities. In these conditions, an important problem that the management has to solve in the process of circulation of medicines is the problem of quality of source information accompanying the business processes of pharmaceutical activities. The source information is not intended for the purposes of analysis, is presented, as a rule, rather raw material that requires preliminary processing by special methods, including their sampling, purification and transformation. The article discusses the advantages of practical application of the capabilities of Loginom analytical platform, which implements the above mentioned methods of pre-processing of raw material and allows solving the tasks of modeling the characteristics of drug circulation. Based on the concept of low-code architecture that provides visual design of complex data analysis scenarios, without involving IT-specialists and realize programming tasks by the business user. The object of the research was the current data obtained from the accounting system of material assets of a wholesale pharmaceutical organization. When organizing a request to the accounting system, based on the considerations of commercial confidentiality, it was decided to limit the 12-month time period and the assortment group, including only antibiotics. During the research, a data processing scenario was developed in the Loginom environment, representing the sequence of actions that need to be carried out to analyze the data. A processing script is a combination of data processing nodes configured by the developer to solve a specific problem. Each script node performs a separate operation on specific data. The drug circulation model scenario developed during the study, consisting of five nodes, includes two nodes with initial data “Receipt Data” and “Consumption Data”, as well as nodes “Data Combination”, “Sorting” and “Grouping”. The developed drug circulation model is an alternative to modeling based on the popular Excel spreadsheet processor, the capabilities of which are limited by the volume of processed data, low productivity on large volumes, the difficulty of automating the execution of sequences of actions, and the inconvenience of reusing processing scripts.

Optimal switching for Networked Control Systems with information multiplexing

In this article, we examine a Networked Control System (NCS) in which the plant and the controller communicate over a network subject to a certain communication constraint. The plant is described by discrete-time nonlinear dynamics subject to bounded disturbances. Due to an overloaded communication network, we assume that the control signal and the information from the plant (the measured output signal) cannot be transmitted simultaneously and are subject to a multiplexing constraint. The goal is to design a switching strategy that allows us to sequentially communicate given these constraints while optimizing a quadratic cost over a finite horizon. Consequently, we proceed by emulation and assume that a controller that satisfies performance requirements is already provided. The resulting optimization problem is observed to be an integer programming problem that is generally NP-complete, i.e., the complexity is exponential in the time horizon considered. To overcome this issue, we provide a different perspective on this problem than what has been presented by the community before. Our main contribution is to reformulate the problem with all its constraints to a form that renders it amenable to apply the discrete-time Pontryagin Maximum Principle to get the necessary conditions for the optimality of the control action sequence. These necessary conditions are then solved numerically by a multiple-shooting method. To validate the approach, we present some illustrative numerical experiments on an inverted pendulum. Different setups are considered and numerically analyzed: usage of a predictor when the output is not transmitted and usage of the previous value of the output when the new value is not transmitted, with or without the choice of non-transmission.

Action sequence learning, habits, and automaticity in obsessive-compulsive disorder

This study investigates the goal/habit imbalance theory of compulsion in obsessive-compulsive disorder (OCD), which postulates enhanced habit formation, increased automaticity, and impaired goal/habit arbitration. It directly tests these hypotheses using newly developed behavioral tasks. First, OCD patients and healthy participants were trained daily for a month using a smartphone app to perform chunked action sequences. Despite similar procedural learning and attainment of habitual performance (measured by an objective automaticity criterion) by both groups, OCD patients self-reported higher subjective habitual tendencies via a recently developed questionnaire. Subsequently, in a re-evaluation task assessing choices between established automatic and novel goal-directed actions, both groups were sensitive to re-evaluation based on monetary feedback. However, OCD patients, especially those with higher compulsive symptoms and habitual tendencies, showed a clear preference for trained/habitual sequences when choices were based on physical effort, possibly due to their higher attributed intrinsic value. These patients also used the habit-training app more extensively and reported symptom relief post-study. The tendency to attribute higher intrinsic value to familiar actions may be a potential mechanism leading to compulsions and an important addition to the goal/habit imbalance hypothesis in OCD. We also highlight the potential of smartphone app training as a habit reversal therapeutic tool.

Action sequence learning, habits, and automaticity in obsessive-compulsive disorder

This study investigates the goal/habit imbalance theory of compulsion in obsessive-compulsive disorder (OCD), which postulates enhanced habit formation, increased automaticity, and impaired goal/habit arbitration. It directly tests these hypotheses using newly developed behavioral tasks. First, OCD patients and healthy participants were trained daily for a month using a smartphone app to perform chunked action sequences. Despite similar procedural learning and attainment of habitual performance (measured by an objective automaticity criterion) by both groups, OCD patients self-reported higher subjective habitual tendencies via a recently developed questionnaire. Subsequently, in a re-evaluation task assessing choices between established automatic and novel goal-directed actions, both groups were sensitive to re-evaluation based on monetary feedback. However, OCD patients, especially those with higher compulsive symptoms and habitual tendencies, showed a clear preference for trained/habitual sequences when choices were based on physical effort, possibly due to their higher attributed intrinsic value. These patients also used the habit-training app more extensively and reported symptom relief post-study. The tendency to attribute higher intrinsic value to familiar actions may be a potential mechanism leading to compulsions and an important addition to the goal/habit imbalance hypothesis in OCD. We also highlight the potential of smartphone app training as a habit reversal therapeutic tool.

The well-worn route revisited: Striatal and hippocampal system contributions to familiar route navigation.

Classic research has shown a division in the neuroanatomical structures that support flexible (e.g., short-cutting) and habitual (e.g., familiar route following) navigational behavior, with hippocampal-caudate systems associated with the former and putamen systems with the latter. There is, however, disagreement about whether the neural structures involved in navigation process particular forms of spatial information, such as associations between constellations of cues forming a cognitive map, versus single landmark-action associations, or alternatively, perform particular reinforcement learning algorithms that allow the use of different spatial strategies, so-called model-based (flexible) or model-free (habitual) forms of learning. We sought to test these theories by asking participants (N = 24) to navigate within a virtual environment through a previously learned, 9-junction route with distinctive landmarks at each junction while undergoing functional magnetic resonance imaging (fMRI). In a series of probe trials, we distinguished knowledge of individual landmark-action associations along the route versus knowledge of the correct sequence of landmark-action associations, either by having absent landmarks, or "out-of-sequence" landmarks. Under a map-based perspective, sequence knowledge would not require hippocampal systems, because there are no constellations of cues available for cognitive map formation. Within a learning-based model, however, responding based on knowledge of sequence would require hippocampal systems because prior context has to be utilized. We found that hippocampal-caudate systems were more active in probes requiring sequence knowledge, supporting the learning-based model. However, we also found greater putamen activation in probes where navigation based purely on sequence memory could be planned, supporting models of putamen function that emphasize its role in action sequencing.

A falls recognition framework based on faster R-CNN and temporal action sequences that can be deployed on home service robots

With the popularity of home service robots (e.g. floor sweepers), robots should be considered to have more features for older adult care. Compared to fixed home monitors with a limited field of view, fall detection with service robots is an ideal solution to keep older adults and disabled people within sight. However, the user’s actions, such as lying on the bed to sleep or slumping on the sofa to rest, cause the traditional fall detection system to generate false alarms, which disrupts the user’s family life. The present work proposed an enhanced faster R-convolutional neural network (CNN) network by incorporating temporal action sequences and fall acceleration computation, demonstrating a reduced misjudgment rate on the service robot platform. Firstly, motion images were captured to obtain the target’s motion area description and action timing at the input stage. Then, the faster R-CNN algorithm was implemented to check the suspected falls further based on the falling acceleration of the detected actions during the training phase. Finally, the proposed temporal action sequences module eliminated the action mistaken for falling. Network training and robotic platform testing demonstrated that the proposed approach distinguished between falls and false alarms, which mitigated the occurrence of false positives. On the service robot platform, experimental results showed that the FAR was 8.19 and processing time was 0.79 s.

An intelligent model to decode students’ behavioral states in physical education using back propagation neural network and Hidden Markov Model

This paper highlights the need for intelligent analysis of students’ behavioral states in physical education tasks. The hand-ring inertial data is used to identify students’ motion sequence states. First, statistical feature extraction is performed based on the acceleration and angular velocity data collected from the bracelet. After completing the filtering and noise reduction of the data, we perform feature extraction by Back Propagation Neural Network (BPNN) and use the sliding window method for analysis. Finally, the classification capability of the model sequence is enhanced by the Hidden Markov Model (HMM). The experimental results indicate that the classification accuracy of student action sequences in physical education exceeds 96% after optimization by the HMM method. This provides intelligent means and new ideas for future student state recognition in physical education and teaching reform.

ПОДХОД К ОЦЕНКЕ ЧАСТОТЫ СПЕКТРАЛЬНОЙ СОСТАВЛЯЮЩЕЙ ЦИФРОВОГО СИГНАЛА ПО СПЕКТРУ С НИЗКИМ РАЗРЕШЕНИЕМ

This paper examines certain methods of obtaining the exact frequency of the spectral component from the signal spectrum with insufficient resolution in order to clarify the accuracy of these methods and their applicability. The basic principles and the sequence of actions are given for the implementation of parabolic interpolation and frequency demodulation. These methods are applied to a synthesized signal containing a frequency component that cannot be determined directly from a low-resolution spectrum. The methods considered differ in the complexity of calculations and the accuracy of the results obtained. An introduction is made about the applicability of the considered methods for various tasks of vibration diagnostics. Purpose of the study. The article examines methods for obtaining an accurate frequency of a spectral component from a signal spectrum with insufficient resolution. The aim of the study is to determine the accuracy and applicability of the described methods. Materials and methods. The following methods were used to achieve the stated goal: parabolic interpolation and frequency demodulation. The article presents the basic principles and sequence of actions for implementing these methods. To verify the effectiveness of the used methods, a signal was synthesized containing a frequency component that cannot be directly determined from a low-resolution spectrum. Results. The methods of parabolic interpolation and frequency demodulation were applied to the synthesized signal. The results showed that the specified methods allow obtaining accurate values of the frequency of the spectral component even at low spectrum resolution. The considered methods differ in computational complexity and the accuracy of the obtained results. Conclusion. Based on the conducted research, it can be concluded that the methods of parabolic interpolation and frequency demodulation are effective for obtaining an accurate frequency of a spectral component from a signal spectrum with insufficient resolution. The results of the study can be used in various vibration diagnostics tasks. The conclusion notes that the choice of a specific method depends on the complexity of the calculations and the required accuracy of the results.

An approach to the implementation of measures to reduce incidents when receiving and handing over weapons and ammunition

In this study, the author analyzes the causes and factors contributing to the occurrence of situations of unprofessional actions of policemen in handling combat hand-held small arms related to an involuntary shot when receiving and preparing it for service or when handing over weapons at the end of service. The author examines in detail such issues as: -ensuring security when receiving and handing over weapons in the duty station; - the lack of a unified approach, in the order of actions performed with weapons when loading and discharging it in the duty station; - the use of a single algorithm of employee actions and schemes for it, as ways to reduce cases of injury when loading and discharging weapons. The subject of the study is the algorithm of actions of employees when handling weapons when receiving and handing them over in the duty station. The object of the author's research is the professional training of employees of the territorial bodies of internal affairs of the Russian Federation. Research methods: generalization, comparative analysis, included observation; system analysis and synthesis of practical experience of fire training and interdisciplinary literature review on the studied problem; sociological methods: expert assessments; mathematical and statistical data processing. The main conclusions of the study are: 1.The causes of accidents with firearms are due to non-compliance with safety measures when handling weapons; 2. The lack of an exact sequence of actions with weapons when loading weapons and discharging them manifests itself in the chaotic execution of these actions. 3. The lack of a single algorithm for loading and unloading weapons is the cause of accidents. 4. The use of the algorithm of the employee's actions when loading weapons before entering service and discharging them upon delivery and the scheme for it, placed in the duty stations in the place for loading and discharging weapons, will minimize the likelihood of an accidental shot. A special contribution of the author is the development of a single algorithm and a scheme for it, which are introduced into the educational process and professional activities of the ATS staff. The novelty of the research lies in the development of a single algorithm for the actions of employees with weapons when receiving and handing them over to the duty station.

Design and application of data security privacy protection technology in dance action sequence arrangement and display system

In view of the excessive consumption of difficult resources in traditional dance choreography, insufficient data in choreography and data leakage crisis in the system, this paper puts forward artificial intelligence technology to study the protection of dance choreography and system data security and privacy. In this paper, the directed graph neural network is used to intelligently arrange the dance movements, and the Hidden Markov Model (HMM) is used to process the dance scene data so that the output data is highly similar to the original data. In the protection of data security and privacy, the method of data encryption is used, and the encrypted data needs to be decrypted by algorithm for reading. The experimental results show that the more complete the dance movement display, the higher the dance recognition rate, and the recognition rate of the dynamic light projection algorithm is greater than 90 % regardless of several sets of dance movements, while the recognition rate of the traditional light projection algorithm is not up to the standard regardless of several sets of movements, and the recognition rate of the point cloud segmentation method is higher than 90 % only in 3–4 sets of dance and 5–6 sets of dance movements.

Development of a solution search method using artificial intelligence

The object of the study is decision support systems. The problem of increasing decision-making efficiency in conditions of uncertainty and a set of different parameters was solved using a bio-inspired algorithm. The subject of the study is the decision-making process in management problems using the heron flock algorithm, the improved genetic algorithm and evolving artificial neural networks. A solution search method using the improved heron flock algorithm is proposed. The study is based on the heron flock algorithm to find a solution regarding the object state. Evolving artificial neural networks are used to train the heron flock algorithm, and an advanced genetic algorithm is used to select the best individuals of the heron flock. The method has the following sequence of actions: – input of initial data; – setting agents on the search plane; – numbering heron agents in the flock; – setting the initial velocity of heron agents; – waiting strategy for heron agents; – aggressive strategy; – checking the discriminatory condition; – selection of the best individuals from the heron flock; – ranking and sorting the obtained solutions; – training heron knowledge bases; – determining the amount of necessary computing resources of the intelligent decision support system. The originality of the proposed method consists in setting heron agents taking into account the uncertainty of the initial data, the noise degree of data about the analysis object state. The method makes it possible to reduce the time for decision-making at the level of 22–26 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems

Ukrainian works of folk art through the prism of scientific attribution

Studying the methods of methodological interactions opens up new opportunities for understanding the meaning, artistic and evaluative value of a particular artwork. The attribution of folk art works is one of the most pressing issues in the scientific discourse. The purpose of the article is to develop an effective research toolkit for the attribution of folk art. The research methodology combines discourse interpretation with a disciplinary approach, using both general and specific scientific methods. Essential to attributing folk art is determining the sequence of various known methods. The study’s main results are derived from a systematic and comprehensive examination of key theoretical and practical aspects of folk art, specifically identifying the most effective scientific methods for its attribution. An important outcome is the development of the article’s methodological structure, focusing on the conceptual framework of folk art theory to understand its morphology. Key methods include historical and geographical, semiotic, and analogical approaches. As a result of the analysis, the article develops a methodological construct that has an analytical logic and is a theoretical justification for the concepts of attribution already existing in art history. The selected methods in the study acquire a constant hierarchy and sequence of actions, taking into account each individual situation, which has its own explicability. This analytical process involves the gradual generation of a strategy, methodology, interpretations, and interpretations that form the final knowledge of a folk artwork, as well as reveal gaps that require further search for materials and facts. In a combination of practical and theoretical methods, a folk artwork becomes an extended system and part of traditional everyday life, revealing its own objective features. The practical value of the work lies in the field of educational training of art experts, and is also important for use in special educational components