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Mango fruit is one of the main fruit commodities that contributes to Taiwan's income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks ("WSNs")-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: "optimal," "sub-optimal," or "not-optimal" conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system. A value function-based will be employed to perform DRL model called deep Q-network (DQN) which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior. The WSNs experiment result indicates the system's accuracy by capturing the real-time environment parameters is 98.39%. Meanwhile, the results of comparative accuracy model experiments of the proposed DQN, individual Q-learning, uniform coverage (UC), and Naïve Bayes classifier (NBC) are 97.60%, 95.30%, 96.50%, and 92.30%, respectively. From the results of the comparative experiment, it can be seen that the proposed DQN used in the study has the most optimal accuracy. Testing with 22 test scenarios for "optimal," "sub-optimal," and "not-optimal" conditions was carried out to ensure the system runs well in the real-world data. The accuracy percentage which is generated from the real-world data reaches 95.45%. From the results of the cost analysis, the system can provide a low-cost system compared to the conventional system. [ABSTRACT FROM AUTHOR]

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project's scheduling aligns with the enterprise's sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses. [ABSTRACT FROM AUTHOR]

This paper explores applications of the coordinated multi-point (CoMP) architecture operation of enhanced node B (eNB) in wireless communication networks featuring device-to-device (D2D) signaling. This is applied to cellular phone coverage for rapid mass transit systems, such as the Taiwan high speed rail transport system, and indoor public environments. The paper is based on formulas pertaining to the link between budget design and guidelines, as well as principles and theories of engineering practice, allowing designers to analyze and fully control the uplink and downlink signals and output power of fiber repeaters linking cellular phones to base stations. Finally, we employ easily installed cellular-over-fiber optic solutions for a small cell enhancement (SCE) system with novel architecture based on a leakage coaxial cable system using LTE-A technology. As a result, we successfully applied enhanced coverage designs for distributed antenna systems. These can be used to create self-organizing networks (SoN) for an Internet of Things. [ABSTRACT FROM AUTHOR]

The aquaculture production sector is one of the suppliers of global food consumption needs. Countries that have a large amount of water contribute to the needs of aquaculture production, especially the freshwater fisheries sector. Indonesia is a country that has a large number of large bodies of water and is the top-five producer of aquaculture production. Technology and engineering continue to be developed to improve the quality and quantity of aquaculture production. One aspect that can be observed is how the condition of fish pond water is healthy and supports fish growth. Various studies have been conducted related to the aquaculture monitoring system, but the problem is how effective it is in terms of accuracy of the resulting output, implementation, and costs. In this research, data fusion (DF) and deep reinforcement learning (DRL) were implemented in an aquaculture monitoring system with temperature, turbidity, and pH parameters to produce valid and accurate output. The stage begins with testing sensor accuracy as part of sensor quality validation, then integrating sensors with wireless sensor networks (WSNs) so they can be accessed in real time. The implemented DF is divided into three layers: first, the signal layer consists of WSNs and their components. Second, the feature layer consists of DRL combined with deep learning (DL). Third, the decision layer determines the output of the condition of the fish pond in "normal" or "not normal" conditions. The analysis and testing of this system look at several factors, i.e., (1) the accuracy of the performance of the sensors used; (2) the performance of the models implemented; (3) the comparison of DF-DRL-based systems with rule-based algorithm systems; and (4) the cost effectiveness compared to labor costs. Of these four factors, the DF-DRL-based aquaculture monitoring system has a higher percentage value and is a low-cost alternative for an accurate aquaculture monitoring system. [ABSTRACT FROM AUTHOR]

This paper employs the approach of the differential model to effectively improve the analysis of particle swarm optimization. This research uses a unified model to analyze four typical particle swarm optimization (PSO) algorithms. On this basis, the proposed approach further starts from the conversion between the differential equation model and the difference equation model and proposes a differential evolution PSO model. The simulation results of high-dimensional numerical optimization problems show that the algorithm's performance can be greatly improved by increasing the step size parameter and using different transformation methods. This analytical method improves the performance of the PSO algorithm, and it is a feasible idea. This paper uses simple analysis to find that many algorithms are improved by using the difference model. Through simple analysis, this paper finds that many AI-related algorithms have been improved by using differential models. The PSO algorithm can be regarded as the social behavior of biological groups such as birds foraging and fish swimming. Therefore, these behaviors described above are an ongoing process and are more suitable for using differential models to improve the analysis of PSO. The simulation results of the experiment show that the differential evolution PSO algorithm based on the Runge–Kutta method can effectively avoid premature results and improve the computational efficiency of the algorithm. This research analyzes the influence of the differential model on the performance of PSO under different differenced conditions. Finally, the analytical results of the differential equation model of this paper also provide a new analytical solution. [ABSTRACT FROM AUTHOR]

The safety of patients and the quality of medical care provided to them are vital for their wellbeing. This study establishes a set of RFID (Radio Frequency Identification)-based systems of patient care based on physiological signals in the pursuit of a remote medical care system. The RFID-based positioning system allows medical staff to continuously observe the patient's health and location. The staff can thus respond to medical emergencies in time and appropriately care for the patient. When the COVID-19 pandemic broke out, the proposed system was used to provide timely information on the location and body temperature of patients who had been screened for the disease. The results of experiments and comparative analyses show that the proposed system is superior to competing systems in use. The use of remote monitoring technology makes user interface easier to provide high-quality medical services to remote areas with sparse populations, and enables better care of the elderly and patients with mobility issues. It can be found from the experiments of this research that the accuracy of the position sensor and the ability of package delivery are the best among the other related studies. The presentation of the graphical interface is also the most cordial among human-computer interaction and the operation is simple and clear. [ABSTRACT FROM AUTHOR]

Traditional PID controllers are widely used in industrial applications due to their simple computational architecture. However, the gain parameters of this simple computing architecture are fixed, and in response to environmental changes, the PID parameters must be continuously adjusted until the system is optimized. This research proposes to use the most important deep reinforcement learning (DRL) algorithm in deep learning as the basis and to modulate the gain parameters of the PID controller with fuzzy control. The research has the ability and advantages of reinforcement learning and fuzzy control and constructs a tracking unmanned wheel system. The mobile robotic platform uses a normalization system during computation to reduce the effects of reading errors caused by the wheeled mobile robot (WMR) of environment and sensor processes. The DRL-Fuzzy-PID controller architecture proposed in this paper utilizes degree operation to avoid the data error of negative input in the absolute value judgment, thereby reducing the amount of calculation. In addition to improving the accuracy of fuzzy control, it also uses reinforcement learning to quickly respond and minimize steady-state error to achieve accurate calculation performance. The experimental results of this study show that in complex trajectory sites, the tracking stability of the system using DRL-fuzzy PID is improved by 15.2% compared with conventional PID control, the maximum overshoot is reduced by 35.6%, and the tracking time ratio is shortened by 6.78%. If reinforcement learning is added, the convergence time of the WMR system will be about 0.5 s, and the accuracy rate will reach 95%. This study combines the computation of deep reinforcement learning to enhance the experimentally superior performance of the WMR system. In the future, intelligent unmanned vehicles with automatic tracking functions can be developed, and the combination of IoT and cloud computing can enhance the innovation of this research. [ABSTRACT FROM AUTHOR]

According to data from the Earth's Volcano and Geological Disaster Reduction Center, a country like Indonesia has experienced many natural disasters, one of which is flooding. Floods are an annual natural disaster, especially on mountain slopes. Mountainous areas experience more dangerous than floods than the urban areas because they can cause other natural disasters, such as landslides and damage the hiking trails. The steep and winding roads minimize and limit the number of officers working in the mountains. Therefore, flood detection and monitoring equipment is needed. The proposed system based on AIoT technology provides real-time flood analysis so that the authorities can monitor residents around mountainous areas and provide early warning. This research focuses on the flood observation system as an early warning system to effectively monitor the flood-prone mountain slopes in real time while taking into account the cost, time efficiency, and safety measurement. The proposed system design includes the integration of sensors into the microcontroller, and the communication between the posts using LoRa and SIM900 sends data to the cloud server via the Internet. All sensor readings for each post are displayed on the app, and alerts are sent via SMS and the app. [ABSTRACT FROM AUTHOR]

With rapidly changing technology, people have more and more requirements for thermal comforts regarding indoor temperature, humidity, and wind speed, and pay more attention to air quality. Indoor air quality has serious effects on the elderly, children, and those with respiratory allergies. Based on the architecture of the Internet of Things smart home, this study constructed an indoor air quality monitoring system to explore how people can live in an environment with good air quality. Among the numerous air quality indices (AQIs), the carbon dioxide index and AQI of the American Society of Heating, Refrigerating and Air-Conditioning Engineers are selected as the indices suitable for this study. The common points of the two indices are combined, and then, based on the data of the Environmental Protection Administration, indoor and outdoor environmental parameters are analyzed, and controllable environment variables are simulated to analyze their effects on air quality. This study designed effective load control using fuzzy control and developed a fuzzy rule base for simulation of the environment variables. Decision logic was used to replace the threshold control of indoor air quality in the past, and a comfortable air quality monitoring system was designed by combining the Arduino Uno development board and ESP8266 Wi-Fi wireless transmission modules. [ABSTRACT FROM AUTHOR]

With the development and progress of technology, people's requirements for living quality are increasingly higher. This study builds an indoor thermal comfort environmental monitoring system through the Internet of Things (IoT) architecture to explore the thermal comfort of people in indoor environments. Then, the applicable indicators are selected from a series of thermal comfort pointers, and the controllable indoor environmental parameters are analyzed and simulated on MATLAB to obtain the impact on the thermal comfort indicators, which can serve as important data to set up the fuzzy rule base. Next, according to the ISO7730 comfort standard and energy saving, three ways to control thermal comfort are proposed. With Arduino UNO as the development substrate, the sensing nodes for the indoor environment are set up, and the wireless sensing network is configured with ESP8266 to transmit the sensing data to the terminal. Monitored by the C# human-machine interface, the controllable load is controlled by wireless remote mode. Finally, the data is stored in the database for follow-up experimentation and analysis. Through actual measurement experiments, the thermal comfort and energy saving effects, under comfort, general, and energy-saving modes, as proposed in this study, are verified to achieve a balance between thermal comfort and energy saving. [ABSTRACT FROM AUTHOR]

This paper explores the quality monitoring experiment by three existing neural network approaches to data fusion in wireless sensor module measurements. There are ten sensors deployed in a sensing area, the digital conversion and weight adjustment of the collected data need to be done. This method can improve the accuracy of the estimated data. In this study, the first neural network approach used to the correction of the sensed data is common Back Propagation Network (BPN). Although this approach leads to a quick convergence, the results are not the expectations and the training data output can't achieve a precise correction of the sensed data. The second approach is Radial Basis Function Network (RBFN), which can effectively achieve a correction of the sensed data and its training data output meets better the actual temperature measurements. The last approach is General Regression Neural Network (GRNN). GRNN and RBFN are both probabilistic neural networks. According to the experimental analysis of this study, GRNN can more accurately and quickly obtain the actual situation. The activity function of the nodes in hidden layers of GRNN adopts Gaussian function and thus GRNN has partial approximation ability. That's why GRNN can learn quickly. Besides, man-made adjustable parameter in GRNN only has one threshold so that the impact on the predicted results caused by man-made subjective assumptions can be avoided and the advantage of fast calculation can help network model be predicted more quickly. [ABSTRACT FROM AUTHOR]

Purpose – This study aims to analyze the inertial weight factor value in the (PSO) algorithm and propose non-linear weights with decreasing strategy to implement the improved PSO (IPSO) algorithm. Using various types of sensors, combined with ZigBee wireless sensor networks and the TCP/IP network. The GPRS/SMS long-range wireless network will sense the measured data analysis and evaluation to create more effective monitoring and observation in a regional environment to achieve an Internet of Things with automated information exchange between persons and things. Design/methodology/approach – This study proposes a wireless sensor network system using ZigBee (PSoC-1605A) chip, sensor and circuit boards to constitute the IOT system. The IOT system consists of a main coordinator (PSoC-1605A), smart grid monitoring system, robotic arm detection warning system and temperature and humidity sensor network. The hardware components communicate with each other through wireless transmission. Each node collects data and sends messages to other objects in the network. Findings – This study employed IPSO to perform information fusion in a multi-sensor network. The paper shows that IPSO improved the measurement preciseness via weight factors estimated via experimental simulations. The experimental results show that the IPSO algorithm optimally integrates the weight factors, information source fusion reliability, information redundancy and hierarchical structure integration in uncertain fusion cases. The sensor data approximates the optimal way to extract useful information from each fusion data and successfully eliminates noise interference, producing excellent fusion results. Practical implications – Robotic arm to tilt detection warning system: Several geographic areas are susceptible to severe tectonic plate movement, often generating earthquakes. Earthquakes cause great harm to public infrastructure, and a great threat to high-tech, high-precision machinery and production lines. To minimize the extent of earthquake disasters and allow managers to deal with power failures, vibration monitoring system construction can enhance manufacturing process quality and stability. Smart grid monitoring system: The greenhouse effect, global energy shortage and rising cost of traditional energy are related energy efficiency topics that have attracted much attention. The aim of this paper is that real-time data rendering and analysis can be more effective in understanding electrical energy usage, resulting in a reduction in unnecessary consumption and waste. Temperature and humidity sensor network system: Environmental temperature and humidity monitoring and application of a wide range of precision industrial production lines, laboratories, antique works of art that have a higher standard of environmental temperature and humidity requirements. The environment has a considerable influence on biological lifeforms. The relative importance of environmental management and monitoring is acute. Originality/value – This paper improves the fixed inertial weight of the original particle swarm optimization (PSO) algorithm. An illustration in the paper indicates that IPSO applies the Internet of Things (IOT) system in monitoring a system via adjusted weight factors better than other existing PSO methods in computing a precise convergence rate for excellent fusion results. [ABSTRACT FROM AUTHOR]

This investigate proposed a innovative Improved Hybrid PSO-GA (IHPG) algorithm which it combined the advantages of the PSO algorithm and GA algorithm. The IHPG algorithm uses the velocity and position update rules of the PSO algorithm and the GA algorithm in selection, crossover and mutation thought. This study explores the quality monitoring experiment by three existing neural network approaches to data fusion in wireless sensor module measurements. There are ten sensors deployed in a sensing area, the digital conversion and weight adjustment of the collected data need to be done. This experiment result can improve the accuracy of the estimated data and reduce the randomness of computing by adjustment optimization of smoothing parameter. According to the experimental analysis, the IHPG is better than the single PSO and GA in comparison the various neural network learning model. [ABSTRACT FROM AUTHOR]

As an innovative as well as an interdisciplinary research project, this study performed an analysis of brain signals so as to establish BrainIC as an auxiliary tool for physician diagnosis. Cognition behavior sciences, embedded technology, system on chips (SOC) design and physiological signal processing are integrated in this work. Moreover, a chip is built for real-time electroencephalography (EEG) processing purposes and a Brain Electrical Activity Mapping (BEAM) system, and a knowledge database is constructed to diagnose psychosis and body challenges in learning various behaviors and signals antithesis by a fuzzy inference engine. This work is completed with a medical support system developed for the mentally disabled or the elderly abled. [ABSTRACT FROM AUTHOR]

This study employed the VeriLite PC-based FPGA platform to improve co-design and co-verification simulation for System-on-chip (SOC) design process in FPGA transactional level modeling. This VeriLite platform is a real-time simulations system. It provided SMIMS powerful Software-VeriComm, VeriInstrument and software developer's kit (SDK), offers an incredible performance and improvement in time-to-market. In this investigation, the main advantages of the system allow designers to use tools such as FPGA, Matlab, and Real-View SOC designers to perform. Users can import the input signals easily from PC into the user-designed circuit on the FPGA of VeriLite and export the output signals to PC for observation. A powerful method is proposed that speeds up the ESL design time with a precise result. VeriLite can be an intuitive and easy to use environment provides to access the functionality of HDL simulation acceleration, IP verification, and HW/SW co-verification. In this study, the propose method allows users to quickly develop a hardware-software co-design/co-verification environment finally. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 374-382, 2012 [ABSTRACT FROM AUTHOR]

This article employed VMD tool and force field simulation theorem for protein folding solution. This technique consists of two components: finished protein folding process and found out active sites for drug docking. The goal of protein folding simulation is to search the global energy minimum location with stability state, and the when the protein is finished the folding task, I can find out the active sites for pre-process of ligand protein docking. This work succeeds in citing minimum energy to support this theme. The VMD is a molecular visualization program for displaying, animating, and analyzing large bimolecular systems using 3D graphics and built-in scripting. In force field, I survey many force statistics methods, such as AMBER, MMFF, this work endeavored to predict the structures of the protein folding. Protein folding is a nonlinear dynamics chaos system; the VMD tool and force field simulation were used to attempt to find the optimal solution with low cost and accelerated folding by using. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 19: 717-723, 2011 [ABSTRACT FROM AUTHOR]

This study applies constructivist principles and Virtual Reality (VR) technologyto the Web-based Interactive Design Graphics ( WebDeGrator ) systemto improve learning computer graphics. The constructivist treatment providesstudents with access to their choice of source content, three-dimensional(3D) modeling tools and interactive behaviors in a virtual environment. Inthis study, we developed a VR-based learning system to simulate and adjustcomputer graphics through sculpture graphic algorithms in real-time. We willdiscuss the relationship between constructivism and VR technology and alsohow the WebDeGrator system encourages student-learning through VRtechnology. [ABSTRACT FROM AUTHOR]