Junzo Watada

Universiti Teknologi PETRONAS | UTP · Department of Computer and Information Sciences

The growing penetration of renewable energy has brought significant challenges for modern power system operation. Academic research and industrial practice show that adjusting unit commitment (UC) scheduling periodically according to new forecasts of renewable power provides a promising way to improve system stability and economy; however, this gre...

The purpose of the current study is to propose a novel meta-heuristic image analysis approach using multi-objective optimization, named ‘Pixel-wise k-Immediate Neighbors’ to identify pores and fractures (both natural and induced, even in the micro-level) in the wells of a hydrocarbon reservoir, which presents better identification accuracy in the p...

Blockchains provide a decentralized, permanent, and verifiable ledger that can record transactions having digital properties, leading to a fundamental shift in various revolutionary scenarios, such as smart cities, eHealth, or eGovernment. Blockchain has a wide variety of applications in healthcare that can enhance mobile health applications, track...

Scenario generation is a pivotal method for providing system operators with a reasonable quantity of power scenarios that are capable of reflecting uncertainties and spatiotemporal processes to make exact and effective decisions for power systems. Aiming at improving the forecasting performance of renewable generation and capturing uncertainty as w...

The popularity of Artificial Intelligence has grown lately with the potential it promises for revolutionizing a wide range of different sectors. To achieve the change, whole community must overcome the Machine Learning (ML) related explainability barrier, an inherent obstacle of current sub symbolism-based approaches, e.g. in Deep Neural Networks,...

The popularity of Artificial Intelligence has grown lately with the potential it promises for revolutionizing a wide range of different sectors. To achieve the change, whole community must overcome the Machine Learning (ML) related explainability barrier, an inherent obstacle of current sub symbolism-based approaches, e.g. in Deep Neural Networks,...

With the increasing penetration of renewable energy, uncertainty has become the main challenge of power systems operation. Fortunately, system operators could deal with the uncertainty by adopting stochastic optimization (SO), robust optimization (RO) and distributionally robust optimization (DRO). However, choosing a good decision takes much exper...

Minimum dominating set (MDS) is a well-known NP-hard fundamental graph theory problem having many applications such as mining social networks and bioinformatics. MDS seeks for the minimum subset of vertices in which every vertex not in the selected subset is adjacent to at least one vertex of this subset. In this study, we consider MDS and a comple...

Unsupervised learning, also known as unsupervised machine learning, analyzes and clusters unlabeled data utilizing machine learning techniques. Without human input, these algorithms discover patterns or groupings in the data. In the domain of abuse and network intrusion detection, interesting objects are often short bursts of activity rather than r...

3D clothing data models have been learned from the real clothing data, but it is difficult to predict the exact segmentation mask of a garment as it varies depending on the size. The accurate segmentation of clothes has become a problem over the last few years due to automatic product detection for enhancing the shopping experience for consumers. T...

This paper presents innovative series and summations derived from the optimized combinations relating to the combinatorics. These series and summations will be useful for the researchers who are involving to solve the scientific problems.

The growing penetration of wind power brings unprecedented randomness and uncertainty on the supply side of power systems, which makes the traditional unit commitment (UC) model uneconomical and unreliable. This paper proposes a rolling mechanism for unit commitment optimization based on wind power scenario generation. First, a novel scenario gener...

In this work, we examine a mixed oligopoly model within the framework of consistent conjectural variations, in which one of the producers is a labor-managed company competing with other private firms, and consumer demand is given by a discontinuous function. Private firms attempt to maximize their net profit while the labor-managed company attempts...

Day-ahead scenario generation of renewable power plays an important role in short-term power system operations due to considerable output uncertainty included. In this paper, a deep renewable scenario generation model using style-based generative adversarial networks followed by a sequence encoder network, is developed to generate accurate and reli...

Semantic segmentation annotation helps train computer vision based Artificial Intelligence models where each image pixel is assigned to a specific object class. The model developers try to identify the features helpful for determining the objects of interest by using various supervised deep learning techniques. However, this is a difficult task due...

Video object segmentation’s primary goal is to automatically extract the principal object(s) in the foreground from the background in videos. The primary focus of the current deep learning-based models is to learn the discriminative representations in the foreground over motion and appearance in small-term temporal segments. In the video segmentati...

Skin cancers are increasing at an alarming rate, and detection in the early stages is essential for advanced treatment. The current segmentation methods have limited labeling ability to the ground truth images due to the numerous noisy expert annotations present in the datasets. The precise boundary segmentation is essential to correctly locate and...

In recent days a lot of activities in Deep Learning demonstrated ability to produce much better than other Machine Learning techniques. Much of the challenge in the Deep Learning is about optimizing the weights and several hyper parameters as it takes lot of computation and time to do. Gradient descent has been most popular technique currently in i...

The Multilevel Inverter (MLI) uses Selective Harmonic Elimination (SHE) of the Pulse Width Modulation (PWM) approach to tackle the fundamental harmonics with the elimination of selected lower harmonics. The optimal switching angle for this PWM method is calculated by solving a set of nonlinear equations. The PWM signal is generated using these angl...

To evaluate the hydrocarbon generation potential, Total Organic Carbon (TOC) of source/reservoir rocks is of vital importance. TOC estimation from well logs is challenging and in laboratory from rock specimens is costly as well as time-consuming. TOC prediction from Passey method is low whereas AI techniques such as Artificial Neu-ral Network (ANN)...

Compliance monitoring for quality of web service (QoWS) has accuracy issues due to uncertain network behaviors. Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Definin...

The most important element for the exploration and development of oil and oil shale is total organic carbon (TOC). TOC estimation is considered a challenge for geologists since laboratory methods are expensive and time-consuming. Therefore, due to the complex and nonlinear relationship between well logs and TOC, researchers have begun to use arti�c...

Accurate scenario forecasting of wind power is crucial to the day-ahead scheduling of power systems with large-scale renewable generation. However, the intermittence and fluctuation of wind energy bring great challenges to the improvement of prediction accuracy. Aiming at precisely modeling the uncertainty in wind power, a novel scenario forecastin...

With the advancement of machine learning and artificial intelligence, the automated estimation of a bed's complex lithology has become one of the most crucial requirements in petroleum engineering because of its important role in reservoir characterization. In the past geophysical modelling, petro-physical analysis, artificial intelligence and seve...

Imputing missing data plays a pivotal role in minimizing the biases of knowledge in computational data. The principal purpose of this paper is to establish a better approach to dealing with missing data. Clinical data often contain erroneous data, which cause major drawbacks for analysis. In this paper, we present a new dynamic approach for managin...

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in...

Wellbore trajectory design is a nonlinear and constrained mathematical optimization problem that is used to build a cost-efficient, safe, and easily reachable trajectory. True measured depth (TMD), torque, and strain energy are used as objective functions to evaluate the wellbore trajectory design in this work. The minimum values of these objective...

The optimized design of wellbore trajectory acts as a key factor in the industries associated with oil and gas. Optimization of three significant parameters i.e. true measured depth, torque, and well strain energy are required for optimum wellbore trajectory. As per the formulation of the problem, affiliated objective functions in this paper rely u...

Whatever the exact figures, world energy consumption, particularly electricity consumption, can increase significantly during couple of decades. It is not possible solely due to demographic pressure, but also due to expansion of living standards within the less developed countries. Hydropower has reached high levels of technical sophistication in p...

For calculating constrained optimization problem various socio/bio-inspired algorithms have adopted a penalty function approach to handle linear and nonlinear constraints. In a general sense, the approach is quite easy to understand, but a precise choice of penalty parameter is very much important. It requires a bunch number of primer preliminaries...

Wind power forecast is playing a significant role in the operation and dispatch of modern power systems. Compared with traditional point forecast methods, interval forecast is able to quantify uncertainties effectively. Unfortunately, the stochastic and intermittent nature of wind power has brought significant challenges to get high quality predict...

Optimization of well placement is one of the main dicult factors in the development process in the oil and gas industry. The well placement optimization is high dimensional, multi-modal and discontinuous. In previous research , conventional and non-conventional optimization techniques have been applied to resolve this problem. However, gradient-fre...

Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologie...

In this paper, information incompleteness in tabular data and rule generation in tabular data are focused on, and the previously proposed important researches are surveyed from the viewpoint of rough sets and data mining. Then, “Rough sets Non-deterministic Information Analysis (RNIA)” and “a NIS-Apriori system” proposed by the authors are describe...

In recent years, fuzzy optimization has been widely adopted to handle the nonstatistical uncertainties in portfolio selection. Meanwhile, various risk measurements, including variance, entropy and value at risk, have been introduced in fuzzy environments to evaluate portfolio risks from different perspectives. In this study, we discuss fuzzy multi-...

This paper presents an innovative computing method and models for optimizing the combination defined in combinatorics. The optimized combination has been derived from the iterative computation of multiple geometric series and summability by specialized approach. The optimized combinatorial technique has applications in science, engineering and mana...

Lithology prediction is considered an essential requirement in the field of petroleum exploration. Since reservoirs consist of complex lithologies, predicting the lithology classes is gradually playing a pivotal role in the geosciences. During drilling operations the advancements of real time data recording have been so common in the petroleum indu...

Due to a variety of possible good types and so many complex drilling variables and constraints, optimization of the trajectory of a complex wellbore is very challenging. There are several types of well such as directional wells, horizontal wells, redrilling wells, complex structure wells, cluster wells, and extended reach wells etcetera. Over the p...

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in...

For shale oil and gas exploration total organic carbon (TOC) is the significant factors where TOC estimation considered as a challenges for geological engineers because direct laboratory coring analysis is costly and time consuming. Passey method and Artificial Intelligence (AI) technique have used on well logs extensively to determine TOC content....

Deep learning concept and algorithm play a pivotal role in solving various complicated problems such as playing games, forecasting economic future values, detecting objects in images. It could break through the bottle neck in conventional methods of neural networks and artificial intelligence. This paper will compare two influential deep learning a...

Data integrity, confidentiality and data loss are the issues that arise during transmission because of the use of an inadequate security scheme. These issues become particularly critical for big data transmission due to its own individual overhead causes. Moreover, multiple executions of distinct security algorithms for maintaining confidentiality...

Well placement optimization is one of the major challenging factors in the field development process of oil and gas industry. The objective function of well placement optimization is considered as high dimensional, discontinuous and multi-model. Over the last decade, both gradient-based and gradient-free optimization methods have been implemented t...

In economic or finance field, one of the most studied issues is to get the best possible return with the minimum risk. The objective of the paper is to select the optimal investment portfolio from SP500 stock market and CBOE Interest Rate 10-Year Bond to obtain the minimum risk in the financial market. For this purpose, the paper consists of the fo...

In the current work, we devised a hybrid method involving a Double-Layer Neural Network (DLNN) for solving a quadratic Bi-Level Programming Problem (BLPP). For an efficient and effective solution of such problems, the proposed potential methodology includes an improved Artificial Bee Colony (ABC) algorithm, a Hopfield Network (HN), and a Boltzmann...

Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limited reserves of fossil fuel and global warming make this topic into the center of discussion and research. In this chapter, we will discuss the use and scope of different quantum inspired computational intelligence (QCI) metho...

Deep learning concept and algorithm play a pivotal role in solving various complicated problems such as playing games, forecasting economic future values, detecting objects in images. It could break through the bottle neck in conventional methods of neural networks and artificial intelligence. This paper will compare two influential deep learning a...

Deep learning concept and algorithm play a pivotal role in solving various complicated problems such as playing games, forecasting economic future values, detecting objects in images, etc. It could break through the bottle neck in conventional methods of neural networks and artificial intelligence. This paper will compare two influential deep learn...

Imputing missing data plays a pivotal role in minimizing the biases of knowledge in computational data. The principal purpose of this paper is to establish a better approach to dealing with missing data. Clinical data often contain erroneous data, which cause major drawbacks for analysis. In this paper, we present a new dynamic approach for managin...

Containerization is revolutionizing the way that many industries operate, provisioning major impact to modern computing technologies because it is extra lightweight, highly portable, energy, resource and storage efficient, cost-effective, performance efficient, and extremely quick during boot up. These often facilitate efficient load balancing, low...

In this research, a quantum computing idea based bat algorithm (QBA) is proposed to solve many-objective combined economic emission dispatch (CEED) problem. Here, CEED is represented using cubic criterion function to reduce the nonlinearities of the system. Along with economic load dispatch, emissions of SO2, NOx , and CO2 are considered as separat...

Unmanned aerial vehicles, more typically known as drones are flying aircrafts that do not have a pilot onboard. For drones to fly through an area without GPS signals, developing scene understanding algorithms to assist in autonomous navigation will be useful. In this paper, various thresholding algorithms are evaluated to enhance scene understandin...

To quantitatively discuss the effects and uncertainties of yield changes in each stock for portfolio selection results and then to provide a more reliable portfolio solution for investors, sensitivity analysis is introduced to improve the multi‐objective portfolio model with fuzzy VaR (SA‐VaR‐FMOPSM). Compared with the existing fuzzy VaR multi‐obje...

Unit commitment is an optimization problem in power systems, which aims to satisfy future load at minimal cost by scheduling the on/off state and output of generation resources like thermal units. One challenge herein is the uncertainties that exist in both supply and demand sides of power systems, which becomes more severe with the growing penetra...

In modern power systems, the increasing penetration of renewable energy provides promising ways to operate the system at low cost and low pollution. Unfortunately, the noticeable uncertainties caused by the forecast errors of renewable generation as well as power load restrict the utilization of renewable energy (e.g. cause wind curtailment), and m...

Taking into account the time-varying, jump and leverage effect characteristics of asset price fluctuations, we first obtain the asset return rate model through the GJR-GARCH model (Glosten, Jagannathan and Rundle-generalized autoregressive conditional heteroskedasticity model) and introduce the infinite pure-jump Levy process into the asset return...

In this study, we developed a computing architecture and algorithms for supporting soft realtime task scheduling in a cloud computing environment through the dynamic provisioning of virtual machines. The architecture integrated three modified soft real-time task scheduling algorithms, namely, Earliest Deadline First (EDF), Earliest Deadline until Z...