People counting in smart buildings is crucial for the efficient management of building systems such as energy, space allocation, efficiency, and occupant comfort. This study investigates the use of two non-invasive binary Passive Infrared (PIR) sensors for estimating the number of people in seven office rooms with different people counting intervals. Previous studies often relied on sensor fusion or more complex signal-based PIR sensors, which increased hardware costs, raised privacy concerns, and added installation complexity. Our approach addresses these limitations by utilizing fewer sensors, reducing hardware costs, and simplifying installation, making it scalable and flexible for different room configurations, while also ensuring high consideration of privacy. Additionally, binary PIR sensors are typically part of smart building systems, eliminating the need for additional sensors. We employed several machine learning methods to analyze motion detected by binary PIR sensors, imp roving the accuracy of people counting estimates. We analyzed important features by extracting event count, duration, and density from sensor data, along with features from the room’s shape, to estimate the number of people. We used different machine learning models for estimating the number of people. Models like Gradient Boosting, XGBoost, MLP, and LGBM demonstrated superior performance for their strong ability to handle complex, non-linear relationships in sensor data, high-dimensional datasets, and imbalanced data, which are common challenges in people counting tasks using PIR sensors. These models were evaluated using performance metrics such as accuracy and F1-score. Additionally, the results show that features such as passage events and the number of detected events, combined with machine learning algorithms, can achieve good accuracy and reliability in people counting.

The state space explosion, a challenge analogous to that encountered in a Petri net (PN), has constrained the extensive study of fuzzy Petri nets (FPNs). Current reasoning algorithms employing FPNs, which operate through forward, backward, and bidirectional mechanisms, are examined. These algorithms streamline the inference process by eliminating irrelevant components of the FPN. However, as the scale of the FPN grows, the complexity of these algorithms escalates sharply, posing a significant challenge for practical applications. To address the state explosion issue, this work introduces a parallel bidirectional reasoning algorithm for an FPN that utilizes reverse and decomposition strategies to optimize the implementation process. The algorithm involves hierarchically dividing a large-scale FPN into two sub-FPNs, followed by a converse operation to generate the reversal sub-FPN for the right-sub-FPN. The detailed mapping between the original and reversed FPNs is thoroughly discussed. Parallel reasoning operations are then conducted on the left-sub-FPN and the resulting reversal right-sub-FPN, with the final result derived by computing the Euclidean distance between the outcomes from the output places of the two sub-FPNs. A case study is presented to illustrate the implementation process, demonstrating the algorithm's significant enhancement of inference efficiency and substantial reduction in execution time.

基于可逆和动态分解机制的层次化FPN并行故障诊断

与Petri网类似,模糊Petri网(fuzzy Petri net, FPN)的研究同样受到状态空间爆炸问题的限制。目前,基于FPN的推理算法主要依赖于正向、反向和双向等机制。这些算法通过消除FPN中不相关的部分来简化推理过程。然而,随着规模的扩大,基于FPN的相关应用算法的复杂度迅速增加,这给基于FPN的推理算法的实际应用带来重大挑战。为解决状态爆炸问题,本文提出一种基于可逆和动态分解机制的FPN双向推理算法,以优化推理过程。该算法将层次化后的FPN分解为左右两个子网;然后,深入分析FPN原网与其逆网元素之间的对应关系,提出FPN逆网生成算法,用于生成右子网的逆网;最后,在左子网与右子网的逆网上同时执行推理算法,通过计算两子网输出位置之间的欧式距离得到最终结果。案例表明,本文提出的推理算法显著提高了推理效率,大幅缩短了执行时间。

We evaluate deep learning architectures for rat pose estimation using a six-camera system, focusing on ResNet and EfficientNet across various depths and augmentation techniques. Among the configurations tested, ResNet 152 with default augmentation provided the best performance when employing a multi-perspective network approach in the controlled experimental setup. It reached a Root Mean Squared Error (RMSE) of 8.74, 8.78, and 9.72 pixels for the different angles. The utilization of data augmentation revealed that less altering yields better performance. We propose potential areas for future research, including further refinement of model configurations, more in-depth investigation of inference speeds, and the possibility of transferring network weights to study other species, such as mice. The findings underscore the potential for deep learning solutions to advance preclinical research in behavioral neuroscience. We suggest building on this research to introduce behavioral recogniti on based on a 3D movement reconstruction, particularly emphasizing the motoric aspects of neurodegenerative diseases. This will allow for the correlation of observable behaviors with neuronal activity, contributing to a better understanding of the brain and aiding in developing new therapeutic strategies.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

Evacuating buildings during various emergencies, such as fire or terrorist attacks, requires high awareness of the environment, quick decision-making, and immediate action. In recent years, the research has increased regarding how to support this process and persons involved by dedicated technical means. However, still, a lot of questions remain unanswered. This contribution presents a concept and the first mobile application design as part of an intuitive evacuation assistance system for evacuation leaders to improve emergency situation awareness of individual leaders and the evacuation team. Using the proposed system, the leaders will get real-time support regarding environmental hazards, evacuation procedures, extra assistance requests, the number of people in the building or their area, etc., improving the overall situation awareness. This can be achieved by a proper sensory infrastructure, intelligent algorithms building an artificial situation awareness, and a user-focused interface design, as introduced in this contribution. The analytical discussion presented in this contribution points out the strength of the concept, and the first tests of sensory infrastructure presented show feasibility for real-world applications.

Evacuating buildings during emergencies like fires or terrorist attacks demands heightened environmental awareness, swift decision-making, and immediate action. In recent years, there has been a surge in research aimed at bolstering this process and assisting individuals involved through dedicated technical means. However, many questions still linger unanswered. This study elaborates on the concept and the initial design of a mobile application, constituting part of an intuitive evacuation assistance system tailored for evacuation leaders to enhance the situation awareness of individual leaders and the evacuation team in emergencies. Through the proposed system, leaders will receive real-time assistance regarding environmental hazards, evacuation procedures, requests for additional assistance, and the count of occupants in the building or specific areas, among other aspects, thereby enhancing overall situational awareness. Achieving this entails implementing a suitable sensory infrastructure, deploying intelligent algorithms to construct an artificial situation awareness, and crafting a user-centric interface design, all detailed in this contribution. The analytical discourse presented in this contribution highlights the concept's strength, while the mobile application's and sensory infrastructure's initial trials demonstrate its practical viability for real-world implementation.

Using Artificial Intelligence-based approaches in safety-critical applications requires special attention during development. For instance, as of the beginning of 2027, European Union regulations mandate certification by a notified body for AI integration in safety-critical machinery applications. Nevertheless, AI-based approaches find application across diverse domains, enhancing system performance. Evacuation Assistance Systems used for evacuating buildings during emergencies like fires or terrorist attacks are examples in this context. In recent years, there has been a surge in research and standardization attempts to provide an assurance base for utilizing AI techniques in safety-critical applications from the technical and legislative perspectives. However, the focus is often reduced to automated driving and robotics, and many questions still need to be answered.This paper presents our research on the certification of AI-based systems. We highlight our effort in determining the relevant international standards that need to be complied with. The contribution of this paper is a certification concept for AI-based systems, where performance and reliability are crucial. The unique overview of state-of-the-art and industrial standards allows a certification attempt for this type of system. It also provides a base for future work beyond the scope of automated driving and robotics, such as assistance systems and building automation.The analytical discourse presented in this contribution justifies and highlights the mapping of standards and techniques to required functionalities and architectural components of the Evacuation Assistance System, supporting the quality and performance, system acceptance, and certification for the dedicated domain and purpose.

This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed.

Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optima easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, the values for both accuracy and convergence speed are higher than other algorithms. The results also indicate that the ICSSOA has an outstanding ability to jump out of the local optimum.

A fuzzy Petri net (FPN) is a powerful tool to model and analyze knowledge-based systems containing vague information. This paper systematically reviews recent developments of the FPN model from the following three perspectives: knowledge representation using FPN, reasoning mechanisms using an FPN framework, and the latest industrial applications using FPN. In addition, some specific modeling and reasoning approaches to FPN to solve the 'state-explosion problem' are illustrated. Furthermore, detailed analysis of the discussed aspects are shown to reveal some interesting findings, as well as their developmental history. Finally, we present conclusions and suggestions for future research directions.