4th Industrial Revolution,New Machine Age, and Management Paradigm Shift

CFIT: Controlled Flight Into Terrain


  • Leader’s easy-going judgment of situations
  • Passionate fervor to maintain current status-quo
    • Law of Inertia
  • Nostalgia for the past success
    • Stability NO.1 policy
    • ET (Exploitation) > ER (Exploration)

4th Industrial Revolution and IIT

  • The 4th Industrial Revolution

    • Intelligent machines
    • Dramatically improved productivity
    • Fundamental changes in industrial structures
    • IIT is a main driver
  • IIT

    • Intelligent information technology
    • Providing high productivity through the law of increasing returns
    • Emphasizing technological innovation rather than large-scale facility investment and labor cost reduction

IIT (Intelligent Information Technology)

  • Definition

    • Technologies capable of dealing with humans’ high-dimensional information process by ICT (information and communications technology)
    • Integration of two components
      • “Intelligence” materialized by AI
      • “Information” based on data and network technology
    • AI technology
      • Weak AI (currently)
      • Strong AI (future)
    • Data and network technology
      • IoT (Internet of things)
      • Mobile
      • Cloud and Big Data

    Nature of IIT

    • Decision-making without human intervention
    • Real-time response
    • Self-Evolution
    • Datafication of things

Industrial Revolution, New Machine Age, and Management Paradigm

New Machine Age

  • AI
  • Robot
  • Intelligence
  • Singularity
  • IoT
  • World of Connectivities between Machine and Humans

IT role in new machine age

Roles in NMA

  • Coordinator between Humana and Machines
  • Facilitator between Humans and Machines
  • Human-Friendly IT

Changes in source of competitiveness

Data & Knowledge

  • Emerging as a new source of competitiveness


  • Power source for self-learning with intelligent algorithms

Competitive companies

  • Capable of building an ecosystem that can secure the big data itself, and have a market leading algorithms that can take advantages of it

Among top 10 companies

  • On the basis of market capitalization worldwide
  • 7 ICT enterprises
    • Apple, Google, MS, Amazon, Facebook, GE (General Electric, 通用电气), China Mobile
    • All of them operates their own ecosystem
    • Active investment in ITT areas

Definitions of AI and Its History

What Does AI Really Do?

  • Knowledge Representation
  • Automated reasoning
  • Planning
  • Machine Learning (adapt to new circumstances)
  • Nature language understanding
  • Machine vision, speech recognition, finding data on the web, robotics, and much more

AI History


Deep Learning & AI

NN (Neural Network)

  • hidden layer makes NN have a great power to learn
  • if many number of hidden layers are used, then it’s DNN, or it’s CNN

Deep Learning

  • Deep learning seeks to learn rich hierarchical representations (i.e features) automatically through multiple stage of feature learning process

Convolutional Neural Network

  • CNN are a special type of NN whose hidden units are only connected to local receptive field. The number of parameters needed by CNN is much smaller.

Three Stages of a Convolutional Layer

  1. Convolution stage
  2. Nonlinearity: a nonlinear transform such as rectified linear or tanh (双曲正切)
  3. Pooling: output a summary statistics of local input, such as max pooling and average pooling


Common pooling operations:

  • Max pooling: reports the maximum output within a rectangular neighborhood.
  • Average pooling: reports the average output of a rectangular neighborhood (possibly weighed by the distance from the central pixel)

CNN/DNN Examples:

  • Image Classification
  • AlphaGO
  • Caption Generation (text by image)
  • Neural Talk and Walk
  • Video description (text by video)
  • Image generation by text
  • Code generation by image

Manufacturing & AI

AI for Factories

AI is a key enable for the next generation of smart manufacturing. It can lead to a disruption in traditional workflows, supply chains, value creation and business models in manufacturing

AI for Factories 4.0

What is AI intended for Factories 4.0?

  1. Hybrid Teams of Human Workers and Collaborative Robots in Smart Factories
  2. Deep Learning for State-based and Predictive Maintenance of Networked Production Machines and for Understanding Human Behaviors of Shop Floor Workers (一线工人)
  3. Semantic Technologies for Worldwide Interoperability of Machine-to-Machine Communication in Smart Factories and Logistics
  4. Human-Aware and Real-Time Production Planning & Scheduling for Multiagent Systems and Dynamic Plan Revision
  5. Intelligent Industrial Assistance Systems for Human Workers: Proactive and Situation-Aware On-line Help and Training on the Shop Floor
  6. Trusted Industrial Data Exchange Hubs and Machine Learning for Industrial Process Mining
  7. Active Digital Product Memories and Digital Twins for Intelligent Asset Tracking and Production Cockpits
  8. Security Technologies for Intelligent Intrusion Detection and Penetration Testing for Smart factories
  9. Long-Term Autonomy and Self-Learning as well as Self-Healing Capabilities of Industrial Components

Challenges for AI in manufacturing

  • Transparent-by-design, auditable-by-design, fairness & non-discrimination-by-design.
  • Fact checking & information flow monitoring & Viz.
  • Causal discovery & digital evidences.
  • Deep model & architecture interpretability
  • AI reproducibility
  • data provenance & usage monitoring

Human Resource Management and AI

![](C:\Users\Administrator\OneDrive\skku\Frontier Issues and Management\Notes\Snipaste_2020-05-05_00-23-35.png)

Roles of AI in the Enterprise

  • Automation of simple and repetitive tasks
  • Enhance worker’s capabilities
  • Improve responsiveness to external events
  • Learn from data

AI Approach to HRM

AI in Recruitment

  • Identify internal talents
  • Applicant tracking systems
  • Bias-reduction
  • Chatbots
  • Personally Insights
  • AI-based analysis of video interviews
  • Companies
  • Automatic response and follow-up scheduling
  • Useful feedback to the rejected candidates

AI in Retention

  • Use external data (job market)
  • Internal roles, promotions, other factors
  • Recommendations on how to mitigate the risk

Other Examples

  • Textio Augmented Writing for better jobpostings
    (exceptional vs extraordinary, managing vs developing, “able to work under pressure”)
  • Humanyze People analytics
    (Organizational Network Analysis, Wearable devices to measure interactions at
    workplace and promote balanced interactions)
  • Pymetrics Recruitement games
    (bias-free, machine learning to determine success factors from the existing employees,
    testing 80 factors through games)

Productivity / Learning / Operations


Question: How to avoid the dark side of AI?

Accounting and AI

The Basic Accounting Equation

Assets = Liabilities + Owners’ Equity

  • The balance sheet is an expanded expression of the accounting equation


  • Revenues are inflows of assets (or reductions in liabilities) in exchange for providing goods and services to customers.


  • Expenses occur when resources are consumed in order to generate revenue.
  • Examples include rent, salaries and wages, insurance, electricity, utilities, and the like.

The Profit and Loss Account

The profit and loss statement shows the trading performance of the business and the distribution of profit. Profit is not equivalent to cash in bank

Hype Cycle Model (HCM)

Technologies Go Through a Life Cycle

Definition of AI

  • AI is a broad term that refers to technologies that make machines “smart.” Organization are investing in AI research and applications to automate, augment, or replicate human intelligence-human analytical and/or decision-making

AI - The Basics: Big Data & Algorithms

Big Data

  • Big data means more than just large amounts of data - big data refers to data (information) that reaches such high volume, variety, velocity and variability that organizations invest in system architectures, tools, and practices specifically designed to handle the data.
  • Volume: scale of data
  • Velocity: analysis of streaming data
  • Variety: different forms of data
  • Veracity: uncertainty of data


  • An algorithm is s set of rules for the machine to follow. An algorithm is what enables a machine to quickly process vast amounts of data that a human cannot reasonably process, or even comprehend. The performance and accuracy of algorithms is very important.
  • “effective calculability” or “effective method”
  • A sequence of instructions, typically to solve a class of problems or perform a computation
  • Algorithms are unambiguous specifications for performing calculation, data processing, automated reasoning, and other tasks.

AI - The Basics: Types of AI

  1. Reactive Machines
    It perceives its environment/situation directly and acts on what it sees. It doesn’t have a concept of the wide world. It can’t form memories or draw on past experiences to affect current decisions. It specializes only in one area
    • Deep Blue
    • AlphaGo
  2. Limited Memory
    Further up on the AI evolutionary ladder: this type considers pieces of past information and adds them to its preprogrammed representations of the world. It has just enough memory or experience to make proper decisions and execute appropriate actions.
    • Self-driving vehicles
    • Chatbots, personal digital assistants
  3. Theory of mind
    Type III AI has the capacity to understand thoughts and emotions which affect human behavior. This type-which can comprehend feelings, motives, intentions, and expectations, and can interact socially-has yet to be built, but would likely be the next class of intelligent machines
    • C-3P0 and R2-D2 from the Star Wars universe
    • Sonny in the 2004 film I, Robot
  4. Self Awareness
    These types of AI can form representations about themselves. An extension of the theory of mind, they are aware of their internal states, can predict the feelings of others, and can make abstractions and inferences. They are the future generation of machines: super intelligent, sentient, and conscious
    • Eva in the 2015 movie Ex Machina
    • Synths in the 2015 TV series Humans

Roles of AI in Accounting

  • No More: book keepers, data entry operators, compliance inspectors
  • Accounting departments overall will be trimmed down and the employees left will be able to focus on more strategic and value adding initiatives, like process improvement, cost control, and capital optimisation.

What skills must CPAs develop or strengthen to be relevant and employable in 2030?

  1. Business advisors and strategists
    • AI will do the number crunching and analysis. The accountant must be able to use these to help drive long term business strategy and value creation.
  2. Specialism in complex accounting niches that are fluid and changing like tax law
  3. Accounting Technicians must upgrade to CPA level as soon as possible because those tasks will soon be fully automated

Business Model & AI - Platform Business

What is Platform?

  • Is a nexus of rules and architecture

  • Is open, allowing regulated participation

  • Actively promotes (positive) interactions among different partners in a multi-sided market

  • Scales much faster than a pipeline business because it does not necessarily bear the costs of external production

Platform beats products/services

Platforms leverage network effects

More users = more value = more users…

  • Business and technology building blocks
  • Resources user in common; network effects
  • Match buyers & suppliers; transact and create value using platform resources


  • A platform business model creates value by facilitating interactions between the different customer segments (eg matchmaking and transaction costs reduction)

  • Platform business models are often labelled “two-sided markets” or ”multi-sided markets”, on the basis of how many customer segments they work with

  • Platforms generate network effects (see below) but also a chicken-and-egg problem in terms of how to attract both sides of the market at the same time (they use “ratcheting” or “zig zag” strategies to achieve this

    Network Effects

  • Direct/same side network effects: the more users in one customer segment will attract more users in that customer segment (users of fax machines; users of an instant messaging system)

  • Indirect/cross-side network effects: the more users in one customer segment will attract more users in the other customer segment (users of videogames and game developers)

Examples of Platforms

  • Apple iPod/iTunes system
  • Windows operating system
  • Google platform
  • Ebay
  • Credit cards
  • Newspapers

Platform business

Definition and network effects

  • A platform business model creates value by facilitating interactions between the different customer segments (eg matchmaking and transaction including search cost reduction)
  • Platform business models are often labelled “two-sided markets” or ”multi-sided markets”, on the basis of how many customer segments they work with
  • The value of the platform grows to the extent that is attracts more users
    • direct/same side network effects: the more users in one customer segment will attract more users in that customer segment (users of fax machines; users of an instant messaging system
    • Indirect/cross-side network effects: the more users in one customer segment will attract more users in the other customer segment (users o videogames and game developers)

Key success factors

  • Attract both customers on board at the same time through a sophisticated price mechanism

    • You might need to subsidize the more price sensitive segment and charge the side that increases its demand more strongly as a result of the other side growth (Xbox, PSP)
    • In markets with high sensitivity to quality, you may need to charge more the side that has to supply quality. In this way, you are able to attract providers of high quality products (Xbox, PSP) and maintain a quality platform that attracts the other side of the market
  • Acquiring new customers at a low cost

  • Retain customers for a long period of time

  • Attract “lead” users, with lower joining prices or agreements not to join rival platforms to build initial momentum


Wii attracts a large group of casual gamers with relatively inexpensive consoles and royalties from game developers

Key points

  • Originally, Nintendo’s Wii value proposition was attractive on non professional gamers because of the “fun factor” and “social interactions” - especially younger children, retirement home communities and families
  • These didn’t need a sophisticated and expensive technology like hardcore gamers but wanted a simple intuitive interface model

Freemium as a business model:

providing the service for free to some customer segments to subsidize other customer segments

  • Skype is an example of platform business model with a freemium component
  • A large base of free users is subsidized by a small base of paying users

Healthcare and AI

Why AI for Healthcare?

  • Definition: use of a computer to model intelligent behavior with minimal human intervention

  • Machine & computer programs are capable of problem solving and learning, like a human brain

  • Natural Language Processing (NLP) and translation

    • Pattern recognition
    • Visual perception and
    • Decision making
  • Machine Learning (ML), one of the most exciting areas for Development of computational approaches to automatically make sense of data

  • Advantage of Machine

    • Can retain information
    • Becomes smarter over time
    • Machine is not susceptible to Sleep deprivation, distractions, information overload and short-term memory loss

The application of AI in medicine has two main branches:

  1. Virtual branch

    • The virtual component is represented by Machine Learning, (also called Deep Learning)-mathematical algorithms that improve learning through experience.
  2. Physical branch

    • Physical objects
    • Medical devices
    • Sophisticated robots for delivery of care (carebots)/ robots for surgery.

Benefits of AI in Healthcare

  • Highly repetitive work
  • Empower doctors
  • Augment the professionals, offering them expertise and assistance.
  • Replace personnel and staffing in medical facilities, particularly in administrative functions,
  • Managing wait times & automating scheduling
  • AI will not replace clinicians

Healthcare Data

  • AI systems can be deployed in healthcare applications

    • they need to be ‘trained’ through data that are generated from clinical activities
      • (ex) screening, diagnosis, treatment assignment
  • clinical data often exist

    • in the form of demographics, medical notes, electronic recordings from medical devices, physical examinations and clinical laboratory and images.

Two Major Data Sources

EP data

  • electrophysiological (EP) data

    • they contain large portions of unstructured narrative texts
      • (ex) clinical notes, that are not directly analyzable
  • Electrophysiology is the branch of physiology

    • that studies the electrical properties of biological cells and tissues.
    • It involves measurements of voltage changes or electric current or manipulations on a wide variety of scales from single ion channel proteins to whole organs like the heart.
    • In neuroscience, it includes measurements of the electrical activity of neurons, and, in particular, action potential activity.
    • They are useful for electrodiagnosis and monitoring.

electronic medical record (EMR)

  • An electronic (digital) collection of medical information about a person that is stored on a computer.
  • EMR includes information about a patient’s health history,
    • such as diagnoses, medicines, tests, allergies, immunizations, and treatment plans.
  • EMR can be seen by all healthcare providers who are taking care of a patient and can be used by them to help make recommendations about the patient’s care.

AI Devices in Healthcare

Two major categories

Machine Learning (ML) techniques

  • that analyze structured data such as imaging, genetic and EP data.
  • In the medical applications, the ML procedures attempt to cluster patients’ traits, or infer the probability of the disease outcomes

Natural language processing (NLP) methods

  • that extract information from unstructured data such as clinical notes/medical data
  • The NLP procedures target at turning text to machine-readable structured data, which can hen be analyzed by ML techniques

Strategic Management and AI

Fundamentals of SM

Canoe Theory

  • Think of your organization as long canoe
  • The canoe has a destination
  • Everyone in the canoe has a seat and paddle
  • Everyone is expected to paddle
  • Those who won’t paddle have to get out of the canoe
  • Those who prevent others from paddling have to re-adjust or get out of the canoe
  • There are no passengers in the canoe
  • The canoe theory understands crisis
  • The canoe theory says you have the right to be happy

Strategic Management Model

  • Scanning
    • Where are we now?
    • Industry Analysis - Competitive Intelligence
    • SWOT Analysis
    • Internal versus
      • External Elements
  • Strategy Formulation
    Where do we want to be?
  • Strategy Implementation
    How do we get there?
  • Measurement/Performance
    How do we measure our progress?

New Entrants and Entry Barriers

  • Absolute cost advantages
  • Access to inputs
  • Government policy
  • Economies of scale
  • Capital requirements
  • Brand identity
  • Switching costs
  • Access to distribution
  • Proprietary products

Buyer Power (Channel and End Consumer)

  • Buyer volume and information
  • Brand identity
  • Price sensitivity
  • Threat of backward integration
  • Product differentiation
  • Substitutes

Supplier Power

  • Supplier concentration
  • Differentiation of inputs
  • Switching costs
  • Threat of forward integration
  • Cost relative to total purchases in industry


  • Switching costs
  • Buyer inclination to substitute
  • Variety of substitutes
  • Price-performance tradeoff of substitutes
  • Necessity for product or service

Degree of Rivalry

  • Exit barriers
  • Industry concentration
  • Fixed costs
  • Industry growth
  • Intermittent overcapacity
  • Switching costs
  • Brand identity
  • Diversity of rivals
  • Corporate stakes

Strategic Management Model

Strategy Formulaiton

  • Where do we want to be?

  • Vision

    • Not Optional
    • Stretch - 30+ years
    • 8-10 words in length
    • Future State
    • Brief and Memorable
    • eg: • “Light the Fire Within”
      • “A Safer Future for All Communities”
      • “See the Mountains – Breathe Freely”
      • To Be the Happiest Place on Earth
      • To Be the World’s Best Quick Service Restaurant
  • Mission

    In the absence of a clearly defined direction one is forced to concentrate on confusion that will ultimately consume you

    • What is our purpose?
    • Describes current state
    • Timeline is 3-5 Years
    • Builds on our distinctive competencies
    • Tends to focus on Core Business
    • 30-35 Words in length
    • eg: • “To Lead All Communities in Disaster Preparedness,
      Mitigation, and Recovery by Maximizing Assistance and
      • “Caltrans Improves Mobility Across California.”
      • To produce superior financial returns for our shareholders as
      we serve our customers with the highest quality
      transportation, logistics, and e-commerce.
  • Values

    • Guiding Principles
    • Help establish Culture
    • Part of Preserving the Core
    • Core Ideology
    • eg: • CHP PRIDE
      • HP WAY
      • J & J Credo
      • “Build the Spirit of the Place”
  • Goals

    • Supports the mission
    • Deals with one issue or item of focus
    • reflects a primary activity or strategic direction
    • Describes the “to be” state
    • “BHAG” (Big Hairy Audacious Goal)
    • Encompasses a long period i.e, at least 3 years
    • eg: • Achieve excellence in the delivery of disaster recovery and
      mitigation programs.
      • Professionally develop our employees as a reflection of DAD’s
      key attributes and values.
      • Increase the supply of housing, especially affordable housing.
      • Become a model for customer service.
      • To provide benefits in correct amounts and issued in a timely
  • Objectives

    • Add specificity beyond Goals
    • Answer the questions
      • What is to be accomplished?
      • When?
    • Should contain the smart element
      • Specific
      • Measurable
      • Aggressive but Attainable
      • Results-Oriented
      • Timeframe
      • eg: By June 30, 2005 achieve 75% rating on the DAD service
        index from all stakeholders.
        • Increase sales growth 6-8% in the next 5 years. (P&G)
        • Cut corporate overhead costs by $30 million per year.
        (Fortune Brands)
        • Operate 6,000 stores by 2010 – up from 3,000 in the year
        1. (Walgreen’s)
          • Reduce greenhouse gases by 10 percent (from a 1990 bast)
          by 2010. (BP Amoco)

Strategic Management Model

Strategy Implementation

  • Most open-ended part of Strategic Mgmt
  • People implement strategies not organizations
  • How do we get there?
  • Work action plans
  • GOOMs
    • Goals: broad, general BHAG
    • Outcomes: desired end result and report performance
    • Objectives: what and when
    • Measures: a quantified unit that assesses progress or achievement

Sentiment Analysis (SA)

A field of study with many names

  • Opinion mining
  • Sentiment analysis
  • Sentiment mining
  • Subjectivity detection
  • Often used synonymously
  • Some shadings in meaning
  • “sentiment analysis” describes the current mainstream task best → I’ll use this term

Aspect-oriented sentiment analysis:

It’s not all good or bad

The analysis method

  • Machine learning

    • Supervised
    • unsupervised
  • Lexicon-based

    • dictionary
      • flat
      • with semantics
  • Discourse analysis

  • Objects, aspects, opinions

    • Object identification
    • Aspect extraction
    • Grouping synonyms
    • Opinion orientation classification
    • Neutral sentiment

Neuro Management

  • A study to optimize management activities by using physiological coming from brain, heart, and eyes

  • Different signals

    • Depending on types of management activities
    • Cognitive performance
      • Leading to individual performance and team performance eventually



  • an electroencephalography monitoring method to record electrical activity of the brain
  • EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain
  • EEG refers to the recording of the brain’s spontaneous electrical activity over a period of time, as recorded from multiple electrodes placed on the scalp
  • potential fluctuations time locked to an event, such as ‘stimulus onset’ or ‘button press’

Spectral content of EEG

  • neural oscillations (popularly called “brain waves”) that can be observed in EEG signals in the frequency domain


Electrocardiography (ECG)

  • the process of producing a electrocardiogram
  • a recording - a graph of voltage versus time - of the electrical activity of the heart using electrodes placed on the skin
  • Detecting the small electrical changes that are a consequence of cardiac muscle depolarization followed by repolarization during each cardiac cycle (heartbeat)

Three main components to a ECG

  • P wave
    which represents the depolarization of the atria
  • QRS complex
    which represents the depolarization of the ventricles
  • T wave
    which represents the repolarization of the ventricles


  • The process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head

  • Time to First Fixation

    • The Time to First Fixation (TTFF) indicates the amount of time that it takes a respondent (or all respondents on average) to look at a specific AOI (area of interest) from stimulus onset
  • Heatmaps

    • visualizations which show the general distribution of gaze points.
    • They are typically displayed as a color gradient overlay on the presented image or stimulus
    • The red, yellow, and green colors represent in descending order the amount of gaze points that were directed towards parts of the image

Oxygen and Neuro Management

  • Availability of oxygen

    • crucial for cognitive processes to be intact
    • a lack of oxygen in the brain leads to lower cognitive performance
  • Concentration on works

  • PFC area

    • Prefrontal cortex

PFC (Prefrontal cortex)

  • the cerebral cortex which covers the front part of the frontal lobe
  • planning complex cognitive behavior,
  • personality expression,
  • decision making,
  • moderating social behave
  • orchestration of thoughts and actions in accordance with internal goals.

Executive Function

  • Abilities to differentiate among conflicting thoughts
  • Abilities to determine• good and bad,
    • better and best,
    • same and different,
    • future consequences of current activities,
    • working toward a defined goal,
    • prediction of outcomes,
    • expectation based on actions,
    • social “control” (the ability to suppress urges that, if not suppressed, could lead to socially unacceptable outcomes).
    • concrete rule learning at higher levels of abstraction


How to quantify cerebral oxygenation and hemodynamics

fMRI: functional magnetic resonance imaging
  • Gold standard for the assessment of brain activity

    • as it offers the advantage to measure functional changes across the whole brain with a high spatial resolution
  • Cons

    • acquisition costs are relatively high
    • susceptible to movement artefacts (e.g. requires rigorous head stabilization)
    • relatively noisy during the measurements
    • a relative low temporal resolution
    • cannot be used in special cohorts (e.g., individual with metallic implants or claustrophobia)
PET: positron-emission-tomography
  • PET allows the assessment of changes in various substances (e.g., glucose)
  • PET scans are relatively expensive
  • Repeated measurements within short time intervals are ethically not feasible due to the use of radioactive tracer substances
EEG: electroencephalography
  • Measures the brain activation directly and non-invasively based of neuroelectric signals of neurons

    • a high temporal resolution
    • suffers from a relatively weak spatial resolution
  • Cons

    • relative susceptible to artefacts (e.g., due to sweat or muscle activity)
    • Time consuming in preparation (e.g., when gel is used)
    • the obtained signals are hard to interpret for non-experts

fNIRS: functional near-infrared spectroscopy

  • functional near-infrared spectroscopy

  • An optical neuroimaging technique that is based on the theory of neurovascular coupling and optical spectroscopy

    • An increase in neural activity causes an increase in the oxygen metabolism, which is necessary to satisfy energetic demands of the neuronal tissue (neurometabolic coupling)
    • Within the neuronal oxygen metabolism, oxygen is consumed to produce energy, leading to a decrease in the concentration of oxyHb and to an increaser in the concentration of deoxyHb
  • Neural activity triggers local changes in cerebral hemodynamics that induce an intensified blood flow to the activated brain regions (neurovascular coupling)

    • since the local supply of oxygen is greater than its consumption, in activated brain region, a higher concentration of oxyHb and a decreased concentration of deoxyHb is to be observed

How fNIRS works

  • Light with different wavelengths in the near-infrared spectrum is emitted by a source on the scalp
    • after the travelling through different layers (skull, cerebrospinal fluid), this light reaches neuronal tissue.
    • Inside the tissue, the light undergoes absorption and scattering that contributes to light attenuation.
  • During absorption, the energy of the photons is transformed into internal energy of the respective medium.
  • Scattering forced the photons to deviate from their initially straight trajectories and increase the length of their travelled.
  • The non-absorbed components of the scattered light can be measured by a detector placed on the head’s surface

Pros of fNIRS

  • Well-situated to investigate the effects of physical activity on cognitive performance and cerebral oxygenation/hemodynamics
  • non-invasiveness
  • a relatively good spatial and temporal
  • portability
  • a low noise level during operation
  • relative low acquisition costs,
  • robustness against motion artefacts that make a strict immobilization or sedation of participants unnecessary
  • the possibility to investigate cortical activity in individuals with metallic implants or claustrophobia,
  • the opportunity to conduct repeated measures in short time intervals (since no radioactive tracer substance as in PET is used)
  • fNIRS can be used to quantify both changes of deoxyHb and of oxyHb.
    • The simultaneous assessment of deoxyHb and oxyHb allows the
      quantification of further markers of cortical activation

fNIRS with VMD (Visual Merchandising Displays)



阅读全文 »


一年多前就找我说要听译的旧梦最近突然开始填坑,要压制韩版蓝光的《鬼怪》,由于蓝光版和 HDTV 版不完全相同,会有一些增加/删减的片段,而增加的这些部分目前是没有字幕的,就找了我来听译这些部分。我觉得需要翻译的部分应该很少,而且梦哥调整时间轴需要挺长时间的(打轴这活一想就累人,哈哈),应该应付的过来,就答应了,正好也借此机会重新把这个博客捡起来,记录一下第一次听译的过程。不过以我的性格不知什么时候(应该很快)就又会弃坑了。

阅读全文 »

​ 之前的blog放在vps上,而近来汇率渐长,已无力负担价格高昂(月付都快30了!)的服务器,所以还是用hexo挂到github page上,只需要一个域名就可以了(省了一万块)。至于怎么搭建网上的教程已经很全了,随便搜一下就有很多。总结一下遇到的坑(我总是犯各种很蠢的问题=。=),虽然觉得没用不过还是记录一下:

阅读全文 »