Junzo Watada

Junzo Watada
Universiti Teknologi Petronas | UTP · Department of Computer and Information Sciences

Professor, Ph.D.

About

859
Publications
106,311
Reads
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7,404
Citations
Additional affiliations
April 2003 - present
Waseda University
Position
  • Professor (Full)
April 1983 - March 1992
Ryukoku University
Position
  • Associate Professsor
April 2003 - present
Waseda University
Position
  • Professor
Education
April 1979 - March 1984
Osaka Prefecture University
Field of study
  • School of Industrial Engineering
April 1945 - March 1970
Osaka City University
Field of study
  • School of Electric Engineering

Publications

Publications (859)
Article
Full-text available
Drones play a pivotal role in various industries of Industry 4.0. For achieving the application of drones in a dynamic environment, finding a clear path for their autonomous flight requires more research. This paper addresses the problem of finding a navigation path for an autonomous drone based on visual scene information. A deep learning-based ob...
Chapter
Medical imaging analysis plays a critical role in the medical field, transforming how diseases are found, diagnosed, and treated. The integration of machine learning and deep learning has dramatically advanced the field of medical image analysis, leading to the creation of more advanced algorithms for improved diagnosis and disease detection. This...
Article
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Deep Neural Learning has unequivocally demonstrated its exceptional capabilities, surpassing the performance of various Machine Learning techniques in recent years. Optimizing the parameters, such as weights, in Deep Neural Learning can be a challenging and time-consuming process due to its computational complexity. While Gradient Descent (GD) is p...
Article
Full-text available
Industries are generating massive amounts of data due to increased automation and interconnectedness. As data from various sources becomes more available, the extraction of relevant information becomes crucial for understanding complex systems’ behavior and performance. The growing volume and complexity of time-series data in diverse industries hav...
Chapter
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The Fourth Industrial Revolution, or Industry 4.0, is transforming the asset management landscape, presenting new challenges and opportunities. The increasing interconnectedness of assets and systems, combined with the growing availability of data, has led to the development of Industry 4.0 platforms for asset management. These platforms provide un...
Article
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Cloud solutions accelerate the large‐scale acceptance of IoT projects. By diminishing the need for maintaining on‐premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in‐home appliances) and opened the doors for large‐scale deployment of IoT applications on the cloud. However, shifting le...
Chapter
We explored advancements in robot-assisted mobility systems and the impact of transportation revolutions. Current developments highlight technological breakthroughs in robotics and related transportation domains, including autonomous vehicles, last-mile delivery, and public transportation. We addressed ethical considerations, regulatory frameworks,...
Chapter
The demand of healthcare services is rising, for e.g., Europe and the US are experiencing shortage of healthcare professionals. Artificial intelligence (AI) holds a promise to assist healthcare professionals in wide range of tasks. There is already a large amount of clinical and non-clinical evidence that AI algorithms can analyze both structured a...
Article
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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...
Article
Wind power forecast has played a significant role in modern power systems operation. Meanwhile, interval forecast, as a practical way to represent wind power uncertainty, has attracted considerable attention. In this paper, we propose a novel wind power interval forecast method for multiple wind farms in near regions based on machine learning techn...
Article
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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...
Article
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Most of the world’s oil reserves and natural gas are stored within carbonate rock’s pores and fractures. Pores and fractures are quite popular for predicting the amount of petroleum under an adequate trap condition. Hence, their petrophysical properties, such as shape and size, are paramount for accurately predicting the reservoir’s state and condi...
Chapter
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...
Article
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...
Chapter
Full-text available
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,...
Conference Paper
Full-text available
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,...
Article
Full-text available
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...
Article
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...
Chapter
Full-text available
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...
Chapter
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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...
Article
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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.
Article
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...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Article
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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...
Article
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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...
Conference Paper
Full-text available
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...
Article
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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...
Article
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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)...
Article
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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...
Article
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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...
Article
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...
Article
Full-text available
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...
Chapter
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...
Chapter
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...
Article
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...
Article
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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...
Article
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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...
Chapter
Full-text available
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...
Article
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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...
Article
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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-...
Article
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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...
Article
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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...
Article
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...
Article
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...
Article
Full-text available
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....
Article
Full-text available
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...
Article
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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...
Article
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...
Chapter
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...
Article
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Corporations are migrating their legacy software systems towards the cloud environment for amelioration, to avail benefits of the cloud. Long term success of modernizing a legacy software depends on the characteristics of the chosen cloud migration approach. Organizations must think over how strategically imperative is the chosen cloud migration fr...
Article
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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...
Chapter
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...