Amlan Basu

Bio: Amlan Basu is an academic researcher from ITM University. The author has contributed to research in topics: PID controller & Control theory. The author has an hindex of 6, co-authored 21 publications receiving 98 citations. Previous affiliations of Amlan Basu include University of Strathclyde.

Enhancement of Digital PID Controller Performance for Blood Glucose Level of Diabetic Patients using Disparate Tuning Techniques

Abstract: Objectives: To design digital PID controller by using CHR-I and CHR-II tuning techniques, as it helps in finding out the tuning parameters of controllers for a specific system Transformation of analog to digital PID controller using various transformation techniques like first order hold method, impulse-invariant mapping, Tustin approximation and zero-pole mapping equivalents and also the mathematical modeling of blood glucose level, such that a system injects the exact amount of insulin into the body of diabetic patient to maintain his/her glucose level to the normal range Method/Statistical Analysis: The differential equation of the blood glucose level is formulated and then it is converted to three-dimensional Laplace equation using forward Laplace transform Using the Laplace transform the differential equation of the blood glucose is converted into a s-domain equation Then, using the s-domain equation as the equation of the system and the Tuning techniques, CHR-I and CHR-II, the tuning parameters (Kp, Ki and Kd) are acquired Then, it is converted into digital, ie in z-domain, by applying disparate transformation techniques Findings: On analyzing the acquired equation, it is depicted that on tuning the controller with CHR-I tuning technique the system exhibits zero overshoot which is most reliable and efficient for diabetic patient Also, a considerable settling time of 63362 seconds is also achieved Application/Improvement: Therefore, a system that can inject the exact amount of insulin into the patient's blood and bring the blood glucose level to the normal range, by automatically calculating the amount of insulin required, from the available status of blood glucose level, is being achieved

Designing of the PID and FOPID controllers using conventional tuning techniques

Abstract: This paper focuses on the designing of Integer order & fractional order PID controllers. Various tuning techniques are used for the designing of the PID controllers and the Fractional order PID controllers (FOPID). Ziggler Nicholas method, Cohencoon, Astrom-Hagglund (AMIGO), Chien-Hrones-Reswick methods are used to find proportional, derivative & integral parameters of PID & FOPID. Nelder Mead optimization technique is used to determine the specialized fractional order parameters for FOPID. The complete algorithm or the tuning process of each and every method has been explained and discussed. All the designs & their step response is specified & all the characteristics of the systems are discussed.

Indoor home scene recognition using capsule neural networks

Abstract: This paper presents the use of a class of Deep Neural Networks for recognizing indoor home scenes so as to aid Intelligent Assistive Systems (IAS) in performing indoor services to assist elderly or infirm people. Identifying exact indoor location is important so that objects associated with particular tasks can be located speedily and efficiently irrespective of position or orientation. In this way, IAS developed for providing services may become more efficient in accomplishing designated tasks satisfactorily. There are many Convolutional Neural Networks (CNNs) which have been developed for outdoor scene classification and, also, for interior (not necessarily indoor home) scene classification. However, to date, there are no CNNs which are trained, validated and tested on indoor home scene datasets as there appears to be an absence of sufficiently large databases of home scenes. Nonetheless, it is important to train systems which are meant to operate within home environments with the correct relevant data. To counteract this problem, it is proposed that a different type of network is used, which is not very deep (i.e., a network which does not have too many layers) but which can attain sufficiently high classification accuracy using smaller training datasets. A type of neural network likely to help achieve this is a Capsule Neural Network (CapsNet). In this paper, 20,000 indoor home scenes were used for training the CapsNet, and 5000 images were used for testing it. The validation accuracy achieved is 71% and testing accuracy achieved is 70%.

Improvising tuning techniques of digital PID controller for blood glucose level of diabetic patient

Abstract: In this paper the digital PID (Proportional-integral derivative) controller is designed based on controlling the blood glucose level of diabetic patient. The main objective is to design a digital PID controller for blood glucose level of diabetic patient which can inject external insulin to the patient in a accurate controlled way. The blood sugar level of the patient is considered as input variable & injected insulin level is considered as output variable which is to be manipulated. A dynamic model is constructed & transfer function is defined for this system. Using various tuning rules like Cohen-Coon method, Astrom-Hagglund or AMIGO method and Chien-Hrones-Reswick (CHR) method the Proportional, Integral & Derivative coefficient. The tuning responses are studied & parameters are compared. Best response given by the PID is converted into Digital PID. In this paper we also studied different transformation method to convert the conventional PID into the digital PID controller.

Human Emotion Recognition in Video Using Subtraction Pre-Processing

Abstract: In this paper, we describe a new image pre-processing method, which can show features or important information clearly. Deep learning methods have grown rapidly in the last ten years and have better performance than the traditional machine learning methods in many domains. Deep learning shows its powerful ability particular in difficult multi-classes classification challenges. Video Facial expression recognition is one of the most popular classification topics and will become essential in robotics and auto-motion fields. The new system presented is a combination of new video pre-processing and Convolutional Neural Network (CNN). The new pre-processing method is proposed because we believe individual emotions are dynamic, which means the change of the face is the key feature. RAVDESS is the video set used, to train and test the neural network. From RAVDESS dataset the video songs without audio are taken for focusing on video frames differences. The chosen video set has six different classes of emotions. Each video presents a sentence in a melodious way. Based on the chosen video set, the new system with a new pre-processing method has been designed and trained. Later, the classification result of the new method has been compared with others in which the same dataset for video emotion recognition was used.

Challenges for adopting and implementing IoT in smart cities

Abstract: The wider use of Internet of Things (IoT) makes it possible to create smart cities. The purpose of this paper is to identify key IoT challenges and understand the relationship between these challenges to support the development of smart cities.,Challenges were identified using literature review, and prioritised and elaborated by experts. The contextual interactions between the identified challenges and their importance were determined using Interpretive Structural Modelling (ISM). To interrelate the identified challenges and promote IoT in the context of smart cities, the dynamics of interactions of these challenges were analysed using an integrated Matrice d’Impacts Croises Multiplication Appliques a un Classement (MICMAC)-ISM approach. MICMAC is a structured approach to categorise variables according to their driving power and dependence.,Security and privacy, business models, data quality, scalability, complexity and governance were found to have strong driving power and so are key challenges to be addressed in sustainable cities projects. The main driving challenges are complexity and lack of IoT governance. IoT adoption and implementation should therefore focus on breaking down complexity in manageable parts, supported by a governance structure.,This research can help smart city developers in addressing challenges in a phase-wise approach by first ensuring solid foundations and thereafter developing other aspects.,A contribution originates from the integrated MICMAC-ISM approach. ISM is a technique used to identify contextual relationships among definite elements, whereas MICMAC facilitates the classification of challenges based on their driving and dependence power. The other contribution originates from creating an overview of challenges and theorising the contextual relationships and dependencies among the challenges.

Optimal FOPID/PID controller parameters tuning for the AVR system based on sine–cosine-algorithm

Abstract: To enhance the controller performance, an advanced sine–cosine-algorithm (SCA) is employed for Fractional order PID (FOPID) controller tuning in this paper. The SCA-FOPID controller is based on model-based controller design method of physical systems to get better performance. The FOPID controller is designed by SCA optimization technique using the time domain objective function for AVR system. The SCA technique is responsible to optimize five parameters of FOPID controller based on minimum value of objective function of the controller design. The proposed SCA-FOPID controller is design at a global optimum of objective function is acheived. Then, the AVR system has good regulation of terminal voltage at the output to meet desired performance. The proposed method has a good reference tracking ability and frequency responses. This method is compared with the PID and FOPID controller designs of AVR system in the recent years, the proposed SCA-FOPID controller gives an excellent performance from the extensive simulations studies.

Mechanical and Thermal Properties of Interlocking Bricks Utilizing Wasted Polyethylene Terephthalate

Abstract: Plastic bottles are non-biodegradable material made up of Polyethylene Terephthalate (PET) and takes around 450 years to get decomposed. In Malaysia, near 13.2% of plastics contribute to municipal solid waste, where 2.5% is PET. To reduce the waste, interlocking bricks manufacture by waste plastic bottles are used to replace the conventional bricks that use cement and clay. The purpose of this research is to reuse plastic bottles comprised of Polyethylene Terephthalate and Polyurethane binder, by manufacturing interlocking brick that helps to reduce the waste on landfills and the pollution. The plastic bottles were shredded and grinded to a size of 0.75 mm and mixed with the Polyurethane (PU) and the Polymer. The mixed later casted and compacted in the interlocking brick machine mould. The tests performed on the interlocking bricks were compressive strength, impact, flexural strength and thermal conductivity for obtaining the mechanical and thermal properties. The tests values were then keyed into the Response Surface Methodology (RSM) to obtain the optimal PET and PU to verify reliability. Based on the results it is concluded that PET/PU of 60/40 ratio is suitable as non-load bearing masonry brick and recommended to be used as partition walls.