Computational intelligence techniques are being investigated as an extension of the traditional fault diagnosis methods. This article presents, for the first time, a scheme for fault detection and isolation via artificial neural networks and fuzzy logic. It deals with the sensor fault of a three-link selective compliance assembly robot arm robot. A second scheme is proposed for fault detection and accommodation via analytical redundancy, and it deals with the sensor fault of a three-link SCARA robot. These proposed FDI approaches are (...) implemented on Matlab/simulink software and tested under several types of faults. The results show the importance of this process. Then, the sensor faults are detected and isolated successfully. Also, the actuator faults are detected and a fault tolerance strategy is used for reconfigurable control using a sliding-mode observer. (shrink)

We describe a prototype ontology-driven information system (ODIS) that exploits what we call Portion of Reality (POR) representations. The system takes both sensor data and natural language text as inputs and composes on this basis logically structured POR assertions. The goal of our prototype is to represent both natural language and sensor data within a single framework that is able to support both axiomatic reasoning and computation. In addition, the framework should be capable of discovering and representing new kinds of (...) situations and thematic roles, (e.g., roles such as agent, patient and instrument), based on new compositions of existing representations. We applied our prototype in an intelligence analysis use case to test the hypothesis that a framework of this sort can produce usefully structured information from combined natural language and sensor data inputs. We further tested our hypothesis by adding an enhanced US Air Force ontology framework to our ODIS in order to (1) process a collection of sensor data, intel reports, and mission plans; (2) build composite POR representations from these data; and (3) machine analyze the fused results to infer mission threats. (shrink)

Intelligent sensors should be seamlessly, securely, and trustworthy interconnected to enable automated high-level smart applications. Semantic metadata can provide contextual information to support the accessibility of these features, making it easier for machines and humans to process the sensory data and achieve interoperability. The unique overview of sensor ontologies according to the semantic needs of the layers of IoT solutions can serve a guideline of engineers and researchers interested in the development of intelligent sensor-based solutions. The explored (...) trends show that ontologies will play an even more essential role in interlinked IoT systems as interoperability and the generation of controlled linkable data sources should be based on semantically enriched sensory data. (shrink)

In this study, we propose the Ankle-Worn Sensor Sleeve to help people maintain their health. Furthermore, our approach uses this sensor sleeve to improve walking motivation by making the walking motion more sensible. In this research, we pursue the design of a wearable device that users can wear as a “shoe accessory,” with the aim of promoting people’s walking motivation. This device is ankle-worn and generates electricity using the physical flexion and extension of the ankle, detects and acquires the amount (...) of electricity generated, and uses the resulting power to produce sound and light feedback while walking. The Sleeve is designed to support health problems, such as locomotive syndrome and dementia, which are caused by physical inactivity. This article presents the results of a questionnaire survey conducted on 172 people who experienced or observed the device during exhibitions. The results reveal that this device increased people’s interest and motivation to walk, including those who did not like to walk. (shrink)

New technologies such as smart sensors improve rehabilitation processes and thereby increase older adults’ capabilities to participate in social life, leading to direct physical and mental health benefits. Wearable smart sensors for home use have the additional advantage of monitoring day-to-day activities and thereby identifying rehabilitation progress and needs. However, identifying and selecting rehabilitation priorities is ethically challenging because physicians, therapists, and caregivers may impose their own personal values leading to paternalism. Therefore, we develop a discussion template consisting (...) of a series of adaptable questions for the patient–physician encounter based on the capability approach. The goal is to improve geriatric rehabilitation and thereby increase participation in social life and well-being. To achieve this goal, we first analyzed what is considered important for participation on basis of the capability approach, human rights, and ethics of care. Second, we conducted an ethical analysis of each of the four identified dimensions of participation: political, economic, socio-cultural, and care. To improve compliance with rehabilitation measures, health professionals must align rehabilitation measures in an open dialogue with the patient’s aspiration for participation in each dimension. A discussion template based on the capability approach allows for a proactive approach in patient information and stimulates a critical assessment of treatment alternatives while reducing the risk of imposing personal values. (shrink)

“Articulate models” subservient to formal intelligence are imagined to be heterarchies of automata capable of performing the “symbolic (quasi-spatial) syntheses” of Luria (1973), where “quasi-spatial” points to the abstract core of spatiality: the symbol productions, combinations, and substitutions of algebraic reckoning. The alleged cognitive role of internal “topographic images” and of “efference copies” is confronted with this background and denied.

Path selection is one of the key technologies of wireless sensor network. A reasonable choice of coverage path can improve the service quality of WSN and extend the life cycle of WSN. Biogeography-based optimization is widely used in the field of cluster intelligent optimization because its search method has a better incentive mechanism for population evolution. In this paper, the move-in and move-out operation and mutation operation of the BBO algorithm enable WSN to find an efficient routing path. In (...) this paper, simulation experiments are carried out in two scenarios of regular deployment and random deployment of WSN nodes. The experimental results show that the quality of the WSN coverage path solution optimized by the BBO algorithm in the two scenarios is better than that of the particle swarm algorithm and genetic algorithm. (shrink)

This article deals with detection and accommodation of sensor faults in longitudinal dynamics of an F8 aircraft model. Both the detection of the fault and reconfiguration of the failed sensor are done with the help of neural network-based models. Detection of a sensor fault is done with the help of knowledge-based neural network fault detection. Apart from KBNNFD, another neural network model is developed in this article for the reconfiguration of the failed sensor. A model-based approach of the neural network (...) is developed, which uses the radial basis function of the neural network. MBNN successfully does the task of providing analytical redundancy for the aircraft sensor. In this work, both detection and reconfiguration of a fault is done using neural networks. Hence, the control system becomes robust for handling sensor failures near steady state and reconfiguration is also faster. A generalized regression neural network, which is a type of radial basis network, is used for MBNN, which gives very efficient results for function approximation. An F8 aircraft model and C-Star controller, which improves its handling quality, are used for validation of the method involved. Models of F8 aircraft, C-Star controller, KBNNFD, and MBNN were developed using MATLAB/simulink. Successful implementation and simulation results are shown and discussed using Simulink. (shrink)

The development of the systems of artificial intelligence (AI) and digital transformation in general lead to the formation of multitude of autonomous agents of artificial and mixed genealogy, as well as to complex structures in the information and regulatory environment with many opportunities and pathologies and a growing level of uncertainty in making managerial decisions. The situation is complicated by the continuing plurality of understanding of the essence of AI systems. The modern expanded understanding of AI goes back to ideas (...) formulated more than 100 years ago. In official national policy documents on the development of AI, working definitions of AI are preferred. The current stage of AI systems life cycle can be assessed as the completion of the initial period in the development of systems associated with weak AI. The ability of artificial systems to realize themselves as a separate person becomes one of the serious scientific and practical challenges. Attention to the issues of the ethics of AIS indicates the expansion of the diversity of its forms and the beginning of the work in the field of goal-setting. New moral and ethical problems also arise in connection with the possibility of the creation of genuine conscious subjects in the foreseeable future. There is an increasing phenomenon of degradation of natural intelligence. It is required to take into account the issue of the heterogeneity of data generated by humans, electronic sensors and network devices in the dynamic complex environments of the digital economy, the issue of the complexity of the process of co-evolution of AI systems, collective and individual natural consciousness. A special area of opportunities and risks is the development of neurotechnologies. The object of control is digital twins, through which there can be manipulation of real attitudes, preferences, and behavior of individuals. As a result, there are the development of technological capabilities that provoke destructive phenomena as well as the formation of a new class of mass addictions. (shrink)

Big cities and growing industrial areas bring high risk of different kinds of pollutions which would implicate to the quality of life of the society. Discovering and monitoring of polluted areas using autonomous mobile robots is nowadays a frequently considered solution concerning both environmental and human safety problems. Being part of a distributed control system, such robots can help to improve the efficiency of the existing conventional pollution prevention systems. On the other hand, during the last decade, wireless sensor networks (...) have provoked the interest of specialists from different areas by imposing a large number of theoretical and practical challenges related to their implementation. This paper attempts to deal with both issues by presenting a possible way for building robotized WSNs that can be suitable for solving different environmental monitoring tasks. The goal is to transform the WSN into an adaptive sensor system with intelligent behavior that can be used to discover and track spots or areas where given monitored environmental parameters violate defined thresholds. The inclusion of robotized agents into the WSN structure can provide additional flexibility with respect to the installation of the network sensors and allow reliable information gathering and transfer. The proposed algorithmic methods have been tested on a pre-built laboratory prototype of a robotized WSN. The robotized agents have been built using iRobot Create mobile platforms that are additionally equipped with a single-chip computers Gumstix Verdex pro TM XL6P with various expansion modules. (shrink)

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning (...) without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems. Keywords: Hebbian learning; synaptic plasticity; neural networks; self-organization; brain; reinforcement; sensory processing; robot control . (shrink)

Deaf-mute people have much potential to contribute to society. However, communication between deaf-mutes and non-deaf-mutes is a problem that isolates deaf-mutes from society and prevents them from interacting with others. In this study, an information technology intervention, intelligent gloves (IG), a prototype of a two-way communication glove, was developed to facilitate communication between deaf-mutes and non-deaf-mutes. IG consists of a pair of gloves, flex sensors, an Arduino nano, a screen with a built-in microphone, a speaker, and an SD (...) card module. To facilitate communication from the deaf-mutes to the non-deaf-mutes, the flex sensors sense the hand gestures and connected wires, and then transmit the hand movement signals to the Arduino nano where they are translated into words and sentences. The output is displayed on a small screen attached to the gloves, and it is also issued as voice from the speakers attached to the gloves. For communication from the non-deaf-mutes to the deaf-mute, the built-in microphone in the screen senses the voice, which is then transmitted to the Arduino nano to translate it to sentences and sign language, which are displayed on the screen using a 3D avatar. A unit testing of IG has shown that it performed as expected without errors. In addition, IG was tested on ten participants, and it has been shown to be both usable and accepted by the target users. (shrink)

Smart parsimonious and economical ways of irrigation have build up to fulfill the sweet water requirements for the habitants of this world. In other words, water consumption should be frugal enough to save restricted sweet water resources. The major portion of water was wasted due to incompetent ways of irrigation. We utilized a smart approach professionally capable of using ontology to make 50% of the decision, and the other 50% of the decision relies on the sensor data values. The decision (...) from the ontology and the sensor values collectively become the source of the final decision which is the result of a machine learning algorithm. Moreover, an edge server is introduced between the main IoT server and the GSM module. This method will not only avoid the overburden of the IoT server for data processing but also reduce the latency rate. This approach connects Internet of Things with a network of sensors to resourcefully trace all the data, analyze the data at the edge server, transfer only some particular data to the main IoT server to predict the watering requirements for a field of crops, and display the result by using an android application edge. (shrink)

Background Palliative care is an integral part of health care, which in term has become increasingly technologized in recent decades. Lately, innovative smart sensors combined with artificial intelligence promise better diagnosis and treatment. But to date, it is unclear: how are palliative care concepts and their underlying assumptions about humans challenged by smart sensor technologies (SST) and how can care benefit from SST? -/- Aims The paper aims to identify changes and challenges in palliative care due to the use (...) of SST. In addition, normative guiding criteria for the use of SST are developed. -/- Methods The principle of Total Care used by the European Association for Palliative Care (EAPC) forms the basis for the ethical analysis. Drawing on this, its underlying conceptions of the human and its socio-ethical aspects are examined with a phenomenological focus. In the second step, the advantages, limitations, and socio-ethical challenges of using SST with respect to the Total Care principle are explored. Finally, ethical-normative requirements for the application of SST are derived. -/- Results and Conclusion First, SST are limited in their measurement capabilities. Second, SST have an impact on human agency and autonomy. This concerns both the patient and the caregiver. Third, some aspects of the Total Care principle are likely to be marginalized due to the use of SST. The paper formulates normative requirements for using SST to serve human flourishing. It unfolds three criteria according to which SST must be aligned: (1) evidence and purposefulness, (2) autonomy, and (3) Total Care. (shrink)

This paper introduces a concept called task muddiness as a metric for higher intelligence. Task muddiness is meant to be inclusive and expendable in nature. The intelligence required to execute a task is measured by the composite muddiness of the task described by multiple muddiness factors. The composite muddiness explains why many challenging tasks are muddy and why autonomous mental development is necessary for muddy tasks. It facilitates better understanding of intelligence, what the human adult mind can do, and how (...) to build a machine to acquire higher intelligence. The task-muddiness indicates a major reason why a higher biological mind is autonomously developed from autonomous, simple-to-complex experience. The paper also discusses some key concepts that are necessary for understanding the mind and intelligence, such as intelligence metrics, the mode a task is conveyed to the task executor, a human and a machine being a joint task performer in the traditional artificial intelligence (AI), a developmental agent (human or machine) being a sole task performer, and the need for autonomy in task-nonexplicit learning. (shrink)

Dragon boat sport is a traditional activity in China. In recent years, dragon boat sport has become more and more popular around the world. In order to face more challenges, it is urgent for athletes to enhance their own strength. Scientific training methods are particularly important for athletes, and accurate training data are the basis to support scientific training. Traditional mathematical statistic methods neither can sample signals accurately nor can they do real-time analysis and feedback the characteristics to each athlete. (...) In this paper, we use the wearable device with a triaxial accelerometer and heart rate sensor builtin to sample the speed signals and heart rate signals of athletes in various stages of men’s 1000m straight race. Based on the complex network theory, we regard the 23 dragon boat athletes in the dragon boat race as 23 nodes so as to establish a network with 23 nodes and reflect the importance of nodes by measuring the impact of node deletion on the results of the race. The neural network multilayer perceptron model is used for training to obtain the optimal combined value with speed and heart rate for each race stage. The optimal value will be used in the simulated race as the target value to verify if it can help to improve the training efficiency. Experimental results show that the optimal value obtained by this method has a positive effect on the results of the dragon boat race which is beneficial to sports training and tactics planning. (shrink)

Reliable data transfer and energy efficiency are the essential considerations for network performance in resource-constrained underwater environments. One of the efficient approaches for data routing in underwater wireless sensor networks (UWSNs) is clustering, in which the data packets are transferred from sensor nodes to the cluster head (CH). Data packets are then forwarded to a sink node in a single or multiple hops manners, which can possibly increase energy depletion of the CH as compared to other nodes. While several mechanisms (...) have been proposed for cluster formation and CH selection to ensure efficient delivery of data packets, less attention has been given to massive data communication processes with sink node. As such, failure in communicating nodes would lead to a significant network void-holes problem. Considering the limited energy resources of nodes in UWSNs along with the heavy load of CHs in the routing process, this paper proposes a void-holes aware and reliable data forwarding strategy (VHARD-FS) in a proactive mode to control data packets delivery from CH nodes to the sink in UWSNs. In the proposed strategy, each CH node is aware of its neighbor’s performance ranking index to conduct a reliable packet transmission to the sink via the most energy-efficient route. Extensive simulation results indicate that the VHARD-FS outperforms existing routing approaches while comparing energy efficiency and network throughput. This study helps to effectively alleviate the resource limitations associated with UWSNs by extending network life and increasing service availability even in a harsh underwater environment. (shrink)

The design of collaborative robotics, such as driver-assisted operations, engineer a potential automation of decision-making predicated on unobtrusive data gathering of human users. This form of ‘somatic surveillance’ increasingly relies on behavioural biometrics and sensory algorithms to verify the physiology of bodies in cabin interiors. Such processes secure cyber-physical space, but also register user capabilities for control that yield data as insured risk. In this technical re-formation of human–machine interactions for control and communication ‘a dissonance of attribution’ :7684, 2019. https://doi.org/10.1073/pnas.1805770115) (...) is created between perceptions of phenomena, materials and decision-making. This reconfigures relations not only between humans and machines, objects and subjects, but possibly disrupts attributive functions in the social system of Law. What it requires is shifting a legal accountability for action from a sovereignty of the human to a new materialist account based on a ‘cognitive assemblage’ between physiological data, computation and algorithmic sensing. This paper investigates the function of law as a guidance system to acknowledge this account of sensory and algorithmic computation as autonomous ‘sensing agents’ that may be accountable in situations of risk. This assemblage of robotic computation and sensory determination requires a clearer legal differentiation across the current static terminologies of person, property, liability and rights that maintain strict separations of object from subject. To neglect this, we argue, law will solely impute attributions of error to humans despite evidence of operation via mutual control. (shrink)

The Smart Home designed to extend older adults independence is emerging as a clinical solution to the growing ageing population. Nurses will and should play a key role in the development and application of Smart Home technology. Accordingly, conceptual frameworks are needed for nurse scientists who are collaborating with multidisciplinary research teams in developing an intelligent Smart Home that assists with managing older adults’ health. We present a conceptual framework that is grounded in critical realism and pragmatism, informing a (...) unique mixed methodological approach to generating, analyzing, and contextualizing sensor data for clinician‐based machine learning. This framework can guide nurse scientists in knowledge construction as they participate in multidisciplinary health‐assistive Smart Home and artificial intelligence research. In this paper, we review philosophical underpinnings and explicate how this framework can guide nurse scientists collaborating with engineers to develop intelligent health‐assistive Smart Homes. It is critical that clinical nursing knowledge is integrated into Smart Home and artificial intelligence features. A conceptual framework and practical method will provide needed structure for knowledge construction by nurse scientists. (shrink)

Artificial intelligence, as a nonhuman entity, is increasingly used to inform, direct, or supplant nursing care and clinical decision‐making. The boundaries between human‐ and nonhuman‐driven nursing care are blurred with the advent of sensors, wearables, camera devices, and humanoid robots at such an accelerated pace that the critical evaluation of its influence on patient safety has not been fully assessed. Since the pivotal release of To Err is Human, patient safety is being challenged by the dynamic healthcare environment like (...) never before, with nursing at a critical juncture to steer the course of artificial intelligence integration in clinical decision‐making. This paper presents an overview of artificial intelligence and its application in healthcare and highlights the implications which affect nursing as a profession, including perspectives on nursing education and training recommendations. The legal and policy challenges which emerge when artificial intelligence influences the risk of clinical errors and safety issues are discussed. (shrink)

This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. (...) The imperatives of the “participatory turn” in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (shrink)

The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data (...) fusion; recommender systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI. (shrink)

Internet of Things is expanding and evolves into all aspects of the society. Research and developments in the field of IoT have shown the possibility of producing huge volume of data and computation among different devices of the IoT. The data collected from IoT devices are transferred to a central server which can further be retrieved and accessed by the service providers for analyzing, processing, and using. Industrial Internet of Health Things is the expansion of the Internet of Health Things (...) which plays an important role in observing, consulting, monitoring, and treatment process of remote exchange data processes. The linkage of computation and interoperability are supported through various intelligent sensors, controllers, and actuators. The role of parallel computing for efficient and Intelligent Industrial Internet of Health Things is obvious to analyze and process different healthcare situations. A detailed overview of this existing literature is needed through which the research community will provide new solutions for efficient healthcare with the help of IoT based on parallel computing. Therefore, the current study presents a detailed overview of the existing literature for facilitating IIoHT. (shrink)

Policymakers need to start considering the impact smart connected toys (SCTs) have on children. Equipped with sensors, data processing capacities, and connectivity, SCTs targeting children increasingly penetrate pervasively personal environments. The network of SCTs forms the Internet of Toys (IoToys) and often increases children's engagement and playtime experience. Unfortunately, this young part of the population and, most of the time, their parents are often unaware of SCTs’ far-reaching capacities and limitations. The capabilities and constraints of SCTs create severe side (...) effects at the technical, individual, and societal level. These side effects are often unforeseeable and unexpected. They arise from the technology's use and the interconnected nature of the IoToys, without necessarily involving malevolence from their creators. Although existing regulations and new ethical guidelines for artificial intelligence provide remedies to address some of the side effects, policymakers did not develop these redress mechanisms having children and SCTs in mind. This article provides an analysis of the arising side effects of SCTs and contrasts them with current regulatory redress mechanisms. We thereby highlight misfits and needs for further policymaking efforts. (shrink)

Policymakers need to start considering the impact smart connected toys (SCTs) have on children. Equipped with sensors, data processing capacities, and connectivity, SCTs targeting children increasingly penetrate pervasively personal environments. The network of SCTs forms the Internet of Toys (IoToys) and often increases children's engagement and playtime experience. Unfortunately, this young part of the population and, most of the time, their parents are often unaware of SCTs’ far-reaching capacities and limitations. The capabilities and constraints of SCTs create severe side (...) effects at the technical, individual, and societal level. These side effects are often unforeseeable and unexpected. They arise from the technology's use and the interconnected nature of the IoToys, without necessarily involving malevolence from their creators. Although existing regulations and new ethical guidelines for artificial intelligence provide remedies to address some of the side effects, policymakers did not develop these redress mechanisms having children and SCTs in mind. This article provides an analysis of the arising side effects of SCTs and contrasts them with current regulatory redress mechanisms. We thereby highlight misfits and needs for further policymaking efforts. (shrink)

Internet of thing building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT edge applications security and approval. (...) There are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques. (shrink)

Smart lighting systems utilize advanced data, control, and communication technologies and allow users to control lights in new ways. However, achieving user comfort, which should be the focus of smart lighting research, is challenging. One cause is the passive infrared sensor that inaccurately detects human presence to control artificial lighting. We propose a novel classification-integrated moving average model method to solve the problem. The moving average increases the Pearson correlation coefficient of motion sensor features to human presence. The classification model (...) is for a smart lighting intelligent control based on these features. Several classification models are proposed and compared, namely, k-nearest neighbor, support vector machine, decision tree, näive Bayes, and ensemble voting. We build an Internet of things system to collect movement data. It consists of a PIR sensor, a NodeMCU microcontroller, a Raspberry Pi-based platform, a relay, and LED lighting. With a sampling rate of 10 seconds and a collection period of 7 days, the system achieved 56852 data records. In the PC test, movement data from the PIR sensor has a correlation coefficient of 0.36 to attendance, while the MA correlation to attendance can reach 0.56. In an exhaustive search of an optimum classification model, KNN has the best and the most robust performance, with an accuracy of 99.8%. It is more accurate than direct light control decisions based on motion sensors, which are 67.6%. Our proposed method can increase the correlation value of movement features on attendance. At the same time, an accurate and robust KNN classification model is applicable for human presence-based smart lighting control. (shrink)

The protection of cultural relics has always been an important issue in the field of museums and archaeology. With the development of Internet of Things technology, the security system of the museum is more intelligent and integrated. In order for the museum display system to keep up with the intelligent age, this article mainly studies the research and realization of the museum showcase system based on the Internet of Things technology in a smart environment. Before the start of (...) the experiment, we developed the overall design of the system, including three functional modules: temperature module, illumination module, and monitoring module. The experiment is done mainly for system testing. The performance test of the sensor module needs to sample the temperature and humidity sensor to verify the accuracy of the temperature and humidity signal collected in the instrument circuit. The light information collection test uses the ADC sampling inside the CC2530 to obtain the required data and judges whether these temperature, humidity, and light intensity values exceed the preset values. The serial port needs to be initialized to carry out data communication and transmission normally. After the receiving end finishes receiving, the sending end will clear the data buffer to prepare for the next data transmission. The experimental data show that the error between the predicted value and measured value of the temperature system is about 3°C, which is within the allowable error range of the experiment. The results show that the system has perfect functions, is safe and reliable, meets the expected requirements, and has good practicability. (shrink)

It may be of interest to see what can be done by giving a robot no innate knowledge about its environment or its sensors or effectors and only a totally general learning mechanism, such as reinforcement learning, or some information-reduction algorithm, to see what it can learn in various environments. However, it is clear that that is not how biological evolution designs animals, as McCarthy states.

It is a popular view thatpractical deliberation excludes foreknowledge of one's choice. Wolfgang Spohn and Isaac Levi have argued that not even a purely probabilistic self-predictionis available to thedeliberator, if one takes subjective probabilities to be conceptually linked to betting rates. It makes no sense to have a betting rate for an option, for one's willingness to bet on the option depends on the net gain from the bet, in combination with the option's antecedent utility, rather than on the offered (...) odds. And even apart from this consideration, assigning probabilities to the options among which one is choosing is futile since such probabilities could be of no possible use in choice. The paper subjects these arguments to critical examination and suggests that, appearances notwithstanding, practical deliberation need not crowd outself-prediction. (shrink)