Enhanced Intelligent Smart Home Control and Security System Based on Deep Learning Model

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

AUTOMATION AND DIGITALIZATION OF ELECTRIC POWER OBJECTS

The application of automation and digitalization systems for the efficient use of electricity is con-sidered. The theoretical, technical and practical advantages and disadvantages of using this approach in the electric power industry have been determined. Provides an analysis of energy policy on the use of various types of energy sources in different countries. The trends in the de-velopment of the electric power industry associated with the introduction of automation systems are outlined.

Instrumentation and Automation Grounding

In any electrical or electronic systems, unwanted signals known as noise signals are encountered which interact with the true signal and thus affecting signal quality. Noise may enter into a device or system in many forms and have a different order of impacts. Prevention and elimination of noise had attained paramount importance to ensure signal fidelity. This chapter presents a comprehensive analysis on elimination of noise by electronic grounding of instrumentation and automation systems as well as various engineering considerations for the same.

Evaluation of ML-Based Grasping Approaches in the Field of Automated Assembly

Current trends in the manufacturing industry lead to high competitive pressure and requirements regarding process autonomy and flexibility in the production environment. Especially in assembly, automation systems are confronted with a high number of variants. Robot-based processes are a powerful tool for addressing these challenges. For this purpose, robots must be made capable of grasping a variety of diverse components, which are often provided in unknown poses. In addition to existing analytical algorithms, empirical ML-based approaches have been developed, which offer great potentials in increasing flexibility. In this paper, the functionalities and potentials of these approaches will be presented and then compared to the requirements from production processes in order to analyze the status quo of ML-based grasping. Functional gaps are identified that still need to be overcome in order to enable the technology for the use in industrial assembly.

IOT Home Automation

The Internet of Things (IoT) is the most recent technology platform. We may regulate our daily routine work, such as home applications, control, and simple communication systems, using IOT, and improve our digital services. The Internet of Things (IoT) is a system that intelligently adds everyday content information to the internet in order to facilitate communication between objects and humans, as well as among themselves. In this study, we demonstrate how IoT can improve home automation. The relay, Node MCU, which is a network access component, is used in our suggested system. We can control that equipment using an IoT-based system technique. Mobile phones, computers, and tablets are used as user interfaces in home automation systems. They can connect to the home automation network through a wireless internet connection. The user will interact with the system directly via the control interface, while home appliances will be controlled remotely via a mobile app. An additional aspect of the home automation system is that it may be protected remotely. This redesigned design concept provided a home automation system that could be controlled efficiently. Keywords: IOT, home automation, smart home, relay, response time, Node MCU (ESP8266)

OPTIMIZATION OF CUT BRAKING MODES ON THE GRAVITY HUMP

Purpose. At the stage of development of modern information systems for controlling the process of breaking up trains on the humps, the main task is to determine such conditions for cuts' rolling, under which the established safety requirements for breaking up, as well as the requirements for the safety of wagons and cargo in them, are fulfilled. The determination of such rolling modes is undoubtedly a rather complex optimization problem, the solution of which is being addressed by a large number of scientists. In this regard, it can be considered that this problem remains relevant, therefore, the purpose of this research work is to determine such modes of braking of cuts of the train, which ensure their reliable separation when rolling from the top of the hill to the sorting tracks. Methodology. To carry out research on the process of dismantling trains on the hump, the method of simulation was used, and to search for the braking modes of the cuts of the calculation group, the Box complex method was used. Findings. The conditions for separating cuts of the calculated group of the train were investigated, and an iterative procedure was developed to optimize the braking modes of all the cuts of the train, which makes it possible to ensure the maximum value of the minimum interval in the calculated group. In this case, the specified procedure takes into account the intervals between the cuts, both on the arrows and on the retarders of the brake positions of the downhill part of the hill. Originality. It has been established in the work that when determining the modes of braking the cuts of the train, it is necessary to take into account the possible separation of the cuts on all elements of the downhill part of the hill - arrows and retarders. The problem of finding the optimal braking modes for all cuts of the train has been formalized and solved, which, in turn, allow providing reliable conditions for separating adjacent cuts at the turnouts and brake positions of the downhill part of the hill. Practical value. The developed method can be used in the study of the sorting process, as well as in the automation systems for disbanding trains on the hump when determining the braking modes of the cuts.

OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry

Automation of systems emerged since the beginning of 20th century. In the early days, the automation systems were developed with a fixed algorithm to perform some specific task in a repeated manner. Such fixed automation systems are revolutionized in recent days with an artificial intelligence program to take decisions on their own. The motive of the proposed work is to train a textile industry system to automatically detect the defects presence in the generated fabrics. The work utilizes an OverFeat network algorithm for such training process and compares its performances with its earlier version called AlexNet and VGG. The experimental work is conducted with a fabric defect dataset consisting of three class images categorised as horizontal, vertical and hole defects.